Method, apparatus, and computer program product for determining play events and outputting events based on real-time data for proximity, movement of objects, and audio data

ABSTRACT

Systems, methods, apparatuses, and computer readable media are disclosed for determining events and outputting events based on real-time data for location and movement of objects and audio data. In one embodiment, a method is provided for a method of determining play events that at least includes receiving audio data, wherein the audio data is received from at least one of a memory or a sensor; determining an event probability based on comparing the audio data to an audio profile; and generating an event based on the event probability satisfying a predetermined threshold.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.14/204,414, filed on Mar. 11, 2014, and claims priority from and thebenefit of the filing date of U.S. Provisional Patent Application No.61/831,990 filed Jun. 6, 2013, the contents of which are incorporated byreference in their entirety herein.

FIELD

Embodiments discussed herein are related to radio frequency locatingand, more particularly, to systems, methods, apparatuses, computerreadable media and other means for determining events.

BACKGROUND

Producing analysis of performance for sports events and/or teams isgenerally a resource intensive process often involving experiencedindividuals manually reviewing games or recordings of games to compileevents and statistics for a game and the participants. Such analysis maybe error prone as it requires reviewing a large number of participantsmoving among complex formations at each moment of a game. One of thesubcategories of analysis is the determination of plays and the playersinvolved in each.

A number of deficiencies and problems associated with providingperformance analytics are identified herein. Through applied effort,ingenuity, and innovation, exemplary solutions to many of theseidentified problems are embodied by the present invention, which isdescribed in detail below.

BRIEF SUMMARY

Systems, methods, apparatuses, and computer readable media are disclosedfor providing real-time collection and analysis of participant (e.g.,player) performance, events, and statistics during a sporting event orother group activity using a locating system, such as a radio frequencylocating system, in conjunction with real time audio stream as hereindescribed.

Embodiments of the present invention may provide for automaticrecognition of formations, plays, and events during a sporting eventthrough the processing of real time (or near real time) data regardinglocation, change in location, change in acceleration, orientation, audiodata, sensor data, or the like, for participants that comprise a team orare otherwise associated with a sporting event or other group activityand how such data fits models that define the formations, plays, andevents. Once such formations, plays, and events have been defined oridentified they may be used to operate, control, or drive analytics orcontrol systems such as, without limitation, visualization systems, gameoperations systems, camera control systems, team analytics systems,league analytics systems, statistics systems, and XML feed/IM feedsystems.

In one embodiment, a method of determining events is provided, themethod comprising receiving audio data, wherein the audio data isreceived from at least one of a memory or a sensor; determining an eventprobability based on comparing the audio data to an audio profile; andgenerating an event based on event probability satisfying apredetermined threshold.

In another embodiment, the method of determining events furthercomprises receiving a tag location data; determining an eventprobability based on comparing the tag location data to an eventprofile. In another embodiment, the method of determining events furthercomprises associating a time data with the audio data; and associatingthe time data with the location data. In another embodiment, the methodof determining events further comprises synchronizing the audio data andlocation data based on the time data. In another embodiment, the methodof determining events further comprises determining an event probabilitybased on comparing audio data and location data to an event model.

In another embodiment, the method of determining events furthercomprises causing the event to be stored in a memory. In anotherembodiment, the method of determining events further comprises causingthe event to be displayed on a user interface. In another embodiment,the method of determining events further comprises outputting the eventto at least one of the group visualization system, a game operationssystem, a camera control system, a team analytics system, a leagueanalytics system, a statistics system, an XML feed and/or instantmessage feed, and a historical data store/engine.

In some embodiments, an apparatus for determining play events isprovided comprising at least one processor and at least one memoryincluding computer instructions configured to, in cooperation with theat least one processor, cause the apparatus to receive audio data,wherein the audio data is received from at least one of a memory or asensor; determine an event probability based on comparing the audio datato an audio profile; and generate an event based on event probabilitysatisfying a predetermined threshold.

In another embodiment, the apparatus may further comprise the at leastone processor and at least one memory including computer instructionsconfigured to, in cooperation with the at least one processor cause theapparatus to receive a tag location data; and determine an eventprobability based on comparing the tag location to an event profile. Inanother embodiment, the apparatus may further comprise the at least oneprocessor and at least one memory including the computer instructionsconfigured to, associate a time data with the audio data; and associatethe time data with the location data.

In another embodiment, the apparatus may further comprise the at leastone processor and at least one memory including the computerinstructions configured to, synchronize the audio data and location databased on the time data. In another embodiment, the apparatus may furthercomprise the at least one processor and at least one memory includingthe computer instructions configured to, determine an event probabilitybased on comparing audio data and location data to an event model.

In another embodiment, the apparatus may further comprise the at leastone processor and at least one memory including the computerinstructions configured to, cause the event to be stored in a memory. Inanother embodiment, the apparatus may further comprise the at least oneprocessor and at least one memory including the computer instructionsconfigured to, cause the event to be displayed on a user interface.

In another embodiment, the apparatus may further comprise the at leastone processor and at least one memory including the computerinstructions configured to, output the event to at least one of thegroup visualization system, a game operations system, a camera controlsystem, a team analytics system, a league analytics system, a statisticssystem, an XML feed and/or instant message feed, and a historical datastore/engine.

In some embodiments, a computer program product for monitoringparticipants is provided, the computer program product comprising anon-transitory computer readable storage medium and computer programinstructions stored therein, the computer program instructionscomprising program instructions at least configured to receive audiodata, wherein the audio data is received from at least one of a memoryor a sensor; determine an event probability based on comparing the audiodata to an audio profile; and generate an event based on eventprobability satisfying a predetermined threshold.

In another embodiment, the computer program product may furthercomprises computer program instructions at least configured to receive atag location data; determine an event probability based on comparing thetag location data to an event profile. In another embodiment, thecomputer program product may further comprises computer programinstructions at least configured to associate a time data with the audiodata; and associate the time data with the location data.

In another embodiment, the computer program product may furthercomprises computer program instructions at least configured tosynchronize the audio data and location data based on the time data. Inanother embodiment, the computer program product may further comprisescomputer program instructions at least configured to determine an eventprobability based on comparing audio data and location data to an eventmodel.

In another embodiment, the computer program product may furthercomprises computer program instructions at least configured to cause theevent to be stored in a memory. In another embodiment, the computerprogram product may further comprises computer program instructions atleast configured to cause the event to be displayed on a user interface.

In another embodiment, the computer program product may furthercomprises computer program instructions at least configured to outputthe event to at least one of the group visualization system, a gameoperations system, a camera control system, a team analytics system, aleague analytics system, a statistics system, an XML feed and/or instantmessage feed, and a historical data store/engine.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 illustrates an exemplary environment using a radio frequencylocating system and audio sensors for determining an event in accordancewith some embodiments of the present invention;

FIGS. 2A-C illustrate some exemplary participants carrying tags andaudio sensors that may provide information for event determination inaccordance with some embodiments of the present invention;

FIGS. 3A-3E are block diagrams showing the input and output of receiversand sensor receivers in accordance with an example embodiment;

FIG. 4 illustrates an exemplary system for providing event data inaccordance with some embodiments of the present invention;

FIG. 5 illustrates example participant tracking over time in accordancewith some embodiments of the present invention;

FIG. 6 illustrates a flowchart of an exemplary process for determiningan event based on audio data and/or location data in accordance withsome embodiments of the present invention; and

FIG. 7 illustrates a block diagram of components that may be included inan apparatus that may determine event data in accordance with some ofthe embodiments of the present invention.

DETAILED DESCRIPTION

The present invention now will be described more fully hereinafter withreference to the accompanying drawings, in which some, but not allembodiments of the inventions are shown. Indeed, the invention may beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein; rather, these embodiments areprovided so that this disclosure will satisfy applicable legalrequirements. Like numbers refer to like elements throughout.

Overview

Existing performance analytics of sporting events have drawbacks inproviding accurate data about events and participant actions that occurduring a game. Game day data is often manually collected by individualsdocumenting play events, e.g., participant actions and playparticipation during a game. Performance review of a game often requiresindividuals to manually review game recordings over a number of hoursafter a game to compile player actions and events during play. Thisperformance review is also often limited to statistics and data that canbe identified or captured by the individuals watching or reviewing agame or game film. In addition, such performance reviews and anyanalytics data flowing therefrom are provided freely on a non-exclusivebasis as anyone with access to game film can compile similar analyticsdata.

Embodiments of the present invention are directed to methods, systems,apparatuses, and computer readable storage media for providing real-timecollection of data and analysis of participant performance and playstatistics during a game such as by using radio frequency locatingsystems and radio frequency identification (“RFID”) in conjunction withaudio data.

Embodiments of the present invention may provide for automaticrecognition of formations, plays, and events through the processing ofreal time data (or near real time data) regarding location, change inlocation, velocity, change in acceleration, orientation, or the like,for participants based on an analysis of relevant models and data asdescribed in detail below. The term “participant” as used herein refersto players, officials, game related objects such as the ball, penaltymarkers, line of scrimmage and yard to gain markers, and any othermovable object proximate a field of play.

In embodiments where participants are players, a group or plurality ofparticipants may be grouped into squads (e.g., offense, defense,kickoff, punt, kick return, punt return, field goal, infield, outfield,bullpen, etc.) and/or teams (e.g., football team, baseball team, swimteam, etc.). Participants on the same team are called team mates;participants on different teams are called adversaries.

Embodiments of the present invention may provide for automated datacollection with reduced errors, as well as providing additionalstatistics that may not be available with current systems. Additionally,embodiments may provide for rapid (i.e., near instantaneous) productionof game review documentation (e.g., playbooks). Embodiments of thepresent invention may also provide additional and exclusive data andanalysis that may be securely licensed without concern that similaranalytics may be readily reproduced without a system configured as setforth below.

Embodiments of the present invention may allow for the simultaneoustracking of a plurality of participants and may provide for indicationsof player statistics and/or potential play events in real time (or nearreal time). Such indications may be output to a variety of systemsincluding, without limitation, a visualization system (e.g., an enhancedtelevision broadcast system or computer graphics visualization system),a game operations system, a camera control system, a team analyticssystem, a league analytics system, and a statistics system.

Embodiments of the present invention are illustrated in the appendedfigures and description below in relation to the sport of Americanfootball. However, as will be apparent to one of ordinary skill in theart in view of this disclosure, the inventive concepts herein describedare not limited to football and may be applied to various otherapplications including, without limitation, other sports or group eventssuch as baseball, basketball, golf, hockey, soccer, racing ormotorsports, competitive events, and the like.

Example RF Locating System Architecture

FIG. 1 illustrates a radio frequency locating system useful fordetermining the location of an object (e.g. a football player on afootball field) by determining RF location tag 102 (e.g., a ultra-wideband (UWB) location tag) location information at each receiver 106(e.g., UWB reader, etc.); a timing reference clock to synchronize thefrequency of counters within each receiver 106; and, in some examples, areference tag 104, preferably a UWB transmitter, positioned at knowncoordinates to enable phase offset between counters to be determined.The systems described herein may be referred to as either“multilateration” or “geolocation” systems; terms which refer to theprocess of locating a signal source by solving for the mathematicalintersection of multiple hyperbolae determined by the difference ofarrival times of a signal received at multiple receivers.

In some examples, the system comprising at least the tags 102 and thereceivers 106 is configured to provide two dimensional and/or threedimensional precision localization (e.g., subfoot resolutions), even inthe presence of multipath interference, due in part to the use of shortnanosecond duration pulses whose time-of-flight can be accuratelydetermined using detection circuitry, such as in the receivers 106,which can trigger on the leading edge of a received waveform. In someexamples, this short pulse characteristic allows necessary data to beconveyed by the system at a higher peak power, but lower overall powerlevels, than a wireless system configured for high data ratecommunications, yet still operate within local regulatory requirementswhich may limit overall power levels.

In some examples, the tags 102 may operate with an instantaneous −3 dBbandwidth of approximately 400 MHz and an average transmission ratebelow a 187.5 kHz regulatory cutoff. In such examples, the predictedmaximum range of the system, operating at 6.0 GHz, is roughly 311meters. Such a configuration advantageously satisfies constraintsapplied by regulatory bodies related to peak and average power densities(e.g., effective isotropic radiated power density), while stilloptimizing system performance related to range and interference. Infurther examples, tag transmissions with a −3 dB bandwidth ofapproximately 400 MHz yields, in some examples, an instantaneouspulsewidth of roughly 2.5 nanoseconds which enables a resolution tobetter than 30 centimeters.

Referring again to FIG. 1, the object to be located has an attached RFlocation tag 102, preferably a tag having a UWB transmitter, thattransmits a signal comprising a burst (e.g., 72 pulses at a burst rateof 1 Mb/s), and optionally, a burst having a tag data packet that mayinclude tag data elements that may include, but are not limited to, atag unique identification number (tag UID), other identificationinformation, a sequential burst count, stored tag data, or other desiredinformation for object or personnel identification, inventory control,etc. In some embodiments, the tag data packet may include atag-individual correlator that can be used to associate a specificindividual (e.g., participant) with a specific tag. In some examples,the sequential burst count (e.g., a packet sequence number) from eachtag 102 may be advantageously provided in order to permit, at a receiverhub 108, correlation of time of arrival (TOA) measurement data fromvarious receivers 106.

In some examples, the RF location tag 102 may employ UWB waveforms(e.g., low data rate waveforms) to achieve extremely fine resolutionbecause of their extremely short pulse (i.e., sub-nanosecond tonanosecond, such as a 2 ns (1 ns up and 1 ns down)) durations. As such,the tag data packet may be of a short length (e.g., 72-112 bits in someexample embodiments), that advantageously enables a higher throughputand higher transmission rates. In some examples, higher throughputand/or higher transmission rates may result in larger datasets forfiltering to achieve a more accurate location estimate. In someexamples, rates of up to approximately 2600 updates per second can beaccommodated without exceeding regulatory requirements. Alternatively oradditionally, in some examples, the length of the tag data packets, inconjunction with other system functionality, may also result in a longerbattery life (e.g., a 3.0 v 1 A-hr lithium cell battery may result in atag battery life in excess of 3.8 years).

In some examples, one or more other tags, such as a reference tag 104,may be positioned within and/or about a monitored area or zone, such asmonitored area 100 illustrated herein as a football field. In someexamples, the reference tag 104 may be configured to transmit a signalthat is used to measure the relative phase (e.g., the count offree-running counters) of non-resettable counters within the receivers106.

One or more (preferably four or more) receivers 106 are also atlocations with predetermined coordinates within and/or around themonitored area 100. In some examples, the receivers 106 may be connectedin a “daisy chain” fashion to advantageously allow for a large number ofreceivers 106 to be interconnected over a significant monitored area inorder to reduce and simplify cabling, reduce latency, provide powerand/or the like. Each of the receivers 106 includes a receiver forreceiving transmissions, such as UWB transmissions, and preferably, apacket decoding circuit that extracts a time of arrival (TOA) timingpulse train, transmitter ID, packet number and/or other information thatmay have been encoded in the tag transmission signal (e.g., materialdescription, personal information, etc.) and is configured to sensesignals transmitted by the tags 102 and one or more reference tags 104(if present).

Each receiver 106 includes a time measuring circuit that measures timedifferences of arrival (TDOA) of tag bursts. The time measuring circuitis phase-locked (e.g., phase differences do not change and thereforerespective frequencies are identical) with a common digital referenceclock signal distributed via cable connection from a receiver hub 108having a central timing reference clock generator. The reference clocksignal establishes a common timing reference for the receivers 106.Thus, multiple time measuring circuits of the respective receivers 106are synchronized in frequency, but not necessarily in phase. While theretypically may be a phase offset between any given pair of receivers inthe receivers 106, the offset is readily determined through use of areference tag 104. Alternatively or additionally, each receiver may besynchronized wirelessly via virtual synchronization without a dedicatedphysical timing channel.

In some example embodiments, the receivers 106 are configured todetermine various attributes of the received signal. Since measurementsare determined at each receiver 106, in a digital format, rather thananalog, signals are transmittable to the receiver hub 108.Advantageously, because packet data and measurement results can betransferred at high speeds to a receiver memory, the receivers 106 canreceive and process tag (and corresponding object) locating signals on anearly continuous basis. As such, in some examples, the receiver memoryallows for a high burst rate of tag events (i.e., tag data packets) tobe captured.

Data cables or wireless transmissions may convey measurement data fromthe receivers 106 to the receiver hub 108 (e.g., the data cables mayenable a transfer speed of 2 Mbps). In some examples, measurement datais transferred to the receiver hub at regular polling intervals.

As such, the receiver hub 108 determines or computes tag location (i.e.,object location) by processing TDOA measurements related to multipledata packets detected by the receivers 106. In some example embodiments,the receiver hub 108 may be configured to resolve the coordinates of atag using nonlinear optimization techniques. The receiver hub 108 mayalso be referred to herein as a locate engine or a receiver hub/locateengine.

In some examples, the system described herein may be referred to as an“over-specified” or “over-determined” system. As such, the receiver hub108 may then calculate one or more valid (i.e., most likely) locationsbased on a set of measurements and/or one or more incorrect (i.e., lesslikely) locations. For example, a location may be calculated that isimpossible due the laws of physics (e.g., a tag on a football playerthat travels more than 100 yards in 1 second) or may be an outlier whencompared to other locations. As such one or more algorithms orheuristics may be applied to minimize such error.

One such algorithm for error minimization, which may be referred to as atime error minimization algorithm, may be described as:

$ɛ = {\sum\limits_{j = 1}^{N}\underset{k = {j + 1}}{\overset{N}{\sum\quad}}\left\{ {\left( {t_{j} - t_{k}} \right) - {\frac{1}{c}\begin{bmatrix}{\left\lbrack {\left( {x - x_{j}} \right)^{2} + \left( {y - y_{j}} \right)^{2} + \left( {z - z_{j}} \right)^{2}} \right\rbrack^{\frac{1}{2}} -} \\\left\lbrack {\left( {x - x_{k}} \right)^{2} + \left( {y - y_{k}} \right)^{2} + \left( {z - z_{k}} \right)^{2}} \right\rbrack^{\frac{1}{2}}\end{bmatrix}}} \right\}^{2}}$

where N is the number of receivers, c is the speed of light, x_(j,k),y_(j,k) and z_(j,k) are the coordinates of the receivers and t_(j,k) arethe arrival times received at each of the receivers. Note that only timedifferences may be evaluated at hub 108 in some example embodiments. Thestarting point for the minimization may be obtained by first doing anarea search on a coarse grid of x, y and z over an area defined by theuser and followed by a localized steepest descent search.

Another or second algorithm for error minimization, which may bereferred to as a distance error minimization algorithm, may be definedby:

$ɛ = {\sum\limits_{j = 1}^{N}\left\lbrack {\left\lbrack {\left( {x - x_{j}} \right)^{2} + \left( {y - y_{j}} \right)^{2} + \left( {z - z_{j}} \right)^{2}} \right\rbrack^{\frac{1}{2}} - {c\left( {t_{j} - t_{0}} \right)}} \right\rbrack^{2}}$

where time and location differences are replaced by theirnon-differential values by incorporating an additional unknown dummyvariable, t₀, which represents an absolute time epoch. The startingpoint for this algorithm is fixed at the geometric mean position of allactive receivers. No initial area search is needed, and optimizationproceeds through the use of a DavidonFletcher-Powell (DFP) quasi-Newtonalgorithm in some examples. In other examples, a steepest descentalgorithm may be used.

In order to determine the coordinates of a tag (T), in some examples andfor calibration purposes, a reference tag (e.g., reference tag 104) ispositioned at a known coordinate position (x_(T), y_(T), z_(T)).

In further example embodiments, a number N of receivers {R_(j): j=1, . .. , N} (e.g., receivers 106) are positioned at known coordinates (x_(R)_(j) , y_(R) _(j) , z_(R) _(j) ), which are respectively located atdistances, such as:

d _(j)=√{square root over ((x _(Rj) −x _(T))²+(y _(Rj) −y _(T))²+(z_(Rj) −z _(T))²)}

from a reference tag.

Each receiver R_(j) utilizes, for example, a synchronous clock signalderived from a common frequency time base, such as clock generator.Because the receivers are not synchronously reset, an unknown, butconstant offset O_(j) exists for each receiver's internal free runningcounter. The value of the offset O_(j) is measured in terms of thenumber of fine resolution count increments (e.g., a number ofnanoseconds for a one nanosecond resolution system).

The reference tag is used to calibrate the radio frequency locatingsystem as follows:

The reference tag emits a signal burst at an unknown time τ_(R). Uponreceiving the signal burst from the reference tag, a count N_(R) _(j) asmeasured at receiver R_(j) is given by

N _(R) _(j) =βτ_(R) +O _(j) +βd _(R) _(j) /c

where c is the speed of light and β is the number of fine resolutioncount increments per unit time (e.g., one per nanosecond). Similarly,each object tag T_(i) of each object to be located transmits a signal atan unknown time τ_(i) to produce a count

N _(i) _(j) =β_(τ) _(i) +O _(j) +βd _(i) _(j) /c

at receiver R_(j) where d_(i) _(j) is the distance between the objecttag T_(i) and the receiver at receiver R_(j). Note that τ_(i) isunknown, but has the same constant value for receivers of all receiversR_(j). Based on the equalities expressed above for receivers R_(j) andR_(k) and given the reference tag information, differential offsetsexpressed as differential count values are determined as follows:

${N_{R_{j}} - N_{R_{k}}} = {\left( {O_{j} - O_{k}} \right) + {\beta \left( {\frac{d_{R_{j}}}{c} - \frac{d_{R_{k}}}{c}} \right)}}$${{or}\left( {O_{j} - O_{k}} \right)} = {{\left( {N_{R_{j}} - N_{R_{k}}} \right) - {\beta \left( {\frac{d_{R_{j}}}{c} - \frac{d_{R_{k}}}{c}} \right)}} = \Delta_{j_{k}}}$

Δ_(jk) is constant as long as d_(Rj)−d_(Rk) remains constant, (whichmeans the receivers and tag are fixed and there is no multipathsituation) and β is the same for each receiver. Note that Δ_(jk) is aknown quantity, since N_(R) _(j) , N_(R) _(k) , β, d_(R) _(j) /c, andd_(R) _(k) /c are known. That is, the differential offsets betweenreceivers R_(j) and R_(k) may be readily determined based on thereference tag transmissions. Thus, again from the above equations, foran object tag (T_(i)) transmission arriving at receivers R_(j) andR_(k):

N _(i) _(j) −N _(i) _(k) =(O _(j) −O _(k))+β(d _(i) _(j) /c−d _(i) _(k)/c)=Δ_(j) _(k) β(d _(i) _(j) /c−d _(i) _(k) /c)

or,

d _(i) _(j) −d _(i) _(k) =(c/β)[N _(i) _(j) −N _(i) _(k) −Δ_(j) _(k) ],

The process further includes determining a minimum error value E_(i),for each object tag T_(i), according to the functional relationship:

$E_{i} = {\min\limits_{({x,y,z})}{\sum\limits_{j}{\sum\limits_{k > j}\left\lbrack {\left( {d_{i_{j}} - d_{i_{k}}} \right) - \left( {{{dist}\left( {T_{x,y,z},R_{j}} \right)} - {{dist}\left( {T_{x,y,z},R_{k}} \right)}} \right)} \right\rbrack^{2}}}}$where${{dist}\left( {T_{x,y,z},R_{j}} \right)} = \sqrt{\left( {x_{R_{j}} - x} \right)^{2} + \left( {y_{R_{j}} - y} \right)^{2} + \left( {z_{R_{j}} - z} \right)^{2}}$

is the Euclidean distance between point (x, y, z) and the coordinates ofthe j^(th) receiver R_(j). The minimization solution (x′, y′, z′) is theestimated coordinate location for the i^(th) tag at to.

In an example algorithm, this proceeds according to:

$ɛ = {\sum\limits_{j = 1}^{N}\left\lbrack {\left\lbrack {\left( {x - x_{j}} \right)^{2} + \left( {y - y_{j}} \right)^{2} + \left( {z - z_{j}} \right)^{2}} \right\rbrack^{\frac{1}{2}} - {c\left( {t_{j} - t_{0}} \right)}} \right\rbrack^{2}}$

where each arrival time, t_(j), is referenced to a particular receiver(receiver “1”) as follows:

$t_{j} = {\frac{1}{\beta}\left( {N_{j} - N_{1} - \Delta_{j_{k}}} \right)}$

and the minimization is performed over variables (x, y, z, t₀) to reacha solution (x′, y′, z′, t₀′).

In some example embodiments, the location of a tag 102 may then beoutput to a receiver processing and distribution system 110 for furtherprocessing of the location data to advantageously providevisualizations, predictive analytics, statistics and/or the like.

The exemplary radio frequency locating system of FIG. 1 may be used inproviding performance analytics in accordance with some embodiments ofthe present invention. In the environment of FIG. 1, data may becaptured and analyzed, such as during a sporting event to identifyevents, statistics, and other data useful to a sports team, league,viewer, licensee, or the like. In some embodiments, data associated witha number of objects or participants (e.g., players, officials, balls,game equipment, etc.) on a playing field, such as monitored area 100,may be generated and provided to a performance analytics system. Assuch, as further discussed in connection with FIGS. 2A-C below, eachobject may have one or more attached tags 102 (such as to equipment wornby a player) to be used to track data such as location, change oflocation, speed, or the like of each object. In some embodiments,additional sensors, such as, without limitation, accelerometers,magnetometers, time-of-flight sensors, health sensors, temperaturesensors, moisture sensors, light sensors, or the like, may be attachedto each object to provide further data to the performance analyticssystem. Such additional sensors may provide data to the tag 102, eitherthrough a wired or wireless connection, to be transmitted to thereceivers 106 or the sensors may be configured to transmit data toreceivers (i.e., sensor receivers) separately from tags 102.

One or more of the receivers 106 may receive transmissions from tags 102and transmit the blink data to a receiver hub 108. The receiver hub 108may process the received data to determine tag location for the tags102. The receiver hub 108 may transmit the tag location data to one ormore processors, such as receiver processing and distribution system110. Receiver processing and distribution system 110 may use one or moremodules (e.g., processing engines) and one or more databases to identifythe object each of the tags 102 is associated with, such as a player,official, ball, or the like.

In some embodiments, multiple tags 102 (as well as other sensors) may beattached to the equipment worn by an individual player, official, orother participant. The receiver processing and distribution system 110may use one or more databases to associate the tag identifier (e.g., atag UID) of each tag 102 with each player, official, object, or otherparticipant and correlate the tag location data and/or other tag andsensor derived data for multiple tags 102 that are associated with aparticular player, official, object, or other participant.

In some embodiments, audio sensors 105 are placed on participants and infixed locations around the monitored area 100. Audio sensors 105 mayalso be moved throughout the monitored area (usually associated with atag to determine location) or aimed by nonparticipant personnel. An“audio sensor” is simply one type of “sensor” (e.g., sensor 203discussed below) that is discussed separately in order to illustrate thegeneration and use of audio data in accordance with various embodiments.Audio sensors may comprise, without limitation, shotgun microphones,parabolic microphones, directional microphones, participant mountedmicrophones (e.g., helmet, wristbands, shoulder pads, apparel, etc.),piezoelectric sound sensors, personnel microphones (e.g., mounted to thebody of the participant rather than to apparel), official callmicrophones, and the like. Audio sensors 105 are configured to generateaudio data as defined below.

In one embodiment, audio sensors 105 may be configured to provide audiodata to the tag 102, either through a wired or a wireless connection, tobe transmitted to the receivers 106 as discussed in greater detailbelow. In another embodiment, audio sensors 105 may be configured totransmit data to receivers 106 and/or sensor receivers 166 (shown inFIG. 3B) separately from tags 102.

As discussed in greater detail below, the receiver processing anddistribution system 110 may then use the tag location data and/or othertag, sensor derived data (including audio data) to determine player andplay dynamics, such as a player's location, how the location is changingwith time, orientation, velocity, acceleration, deceleration, totalyardage, or the like. The receiver processing and distribution system110 may also use the tag location data and/or other tag and sensorderived data to determine dynamics for other participants such as theofficials, the ball, penalty markers, line of scrimmage or yards to gainmarkers, or the like, for use in generating data for performanceanalytics. The receiver processing and distribution system 110 may alsouse the data and one or more databases to determine team formations,play activity, events, statistics, or the like, such as by comparing thedata to various models to determine the most likely formation or play orthe events that have occurred during a game. The receiver processing anddistribution system 110 may also use the data to provide statistics orother output data for the players, teams, and the game.

As will be apparent to one of ordinary skill in the art, the inventiveconcepts herein described are not limited to use with the UWB based RFlocating system shown in FIG. 1. Rather, in various embodiments, theinventive concepts herein described may be applied to various otherlocating systems especially those that are configured to provide robustlocation resolution (i.e., subfoot location resolution).

Example Tag/Sensor Positioning and Participant Correlation

FIG. 1 shows a monitored area 100. The monitored area 100 comprises aplurality of positions at one or more time epochs. The plurality ofpositions may be divided into one or more regions, called zones. Eachzone may be described by one or more coordinate systems, such as a localNED (North-East-Down) system, a latitude-longitude system, or even ayard line system as might be used for an American football game. Alocation is a description of a position, or a plurality of positions,within the monitored area. For example, a field marker at theintersection of the south goal line and west out of bounds line at Bankof America Stadium in Charlotte, N.C. could be described as {0,0,0} in alocal NED system, or 35.225336 N 80.85273 W longitude 751 ft. altitudeon a latitude-longitude system, or simply “Panthers Goal Line” in a yardline system. Because different types of locating systems or differentzones within a single locating system may use different coordinatesystems, a Geographical Information System or similar monitored areadatabase may be used to associate location data. One type ofGeographical Information System describing at least a field of play maybe called Field Data.

FIGS. 2A-C illustrate some exemplary participants that may provideinformation to a performance analytics system in accordance with someembodiments of the present invention. FIG. 2A illustrates a player 202(e.g., a football player) wearing equipment having attached tags 102 inaccordance with some embodiments. In particular, the depicted player 202is wearing shoulder pads having tags 102 affixed to opposite sidesthereof. This positioning advantageously provides an elevated broadcastposition for each tag 102 thereby increasing its communicationeffectiveness.

Additional sensors 203 may be attached to equipment worn by player 202,such as accelerometers, magnetometers, time-of-flight sensors, healthmonitoring sensors (e.g., blood pressure sensors, heart monitors,respiration sensors, moisture sensors, temperature sensors), lightsensors, audio sensors, or the like. The additional sensors 203 may beaffixed to shoulder pads, the helmet, the shoes, rib pads, elbow pads,the jersey, the pants, a bodysuit undergarment, gloves, arm bands,wristbands, and the like.

FIG. 2A depicts audio sensors 105 mounted to the player 202 separatelyfrom the 203 for illustration purposes. However, as was discussed above,an audio sensor 105 is simply one type of sensor 203. The depicted audiosensors 105 are attached to equipment worn by the player 202 and mayinclude a piezoelectric sound sensor or microphone. In some embodiments,the audio sensors 105 may be affixed to the helmet, shoulder pads,jersey, and any other position on the player that is described herein asreceiving a sensor.

As discussed in further detail below, the positioning of audio sensors105 on the helmet or jersey of the player may allow the receiverprocessing and distributing system 110 to determine particular sounds(i.e., audio data) produced by or near the player (Quarterback cadence,field directions, impact noise, snap, or the like).

Sensors 203 (including audio sensors 105) may be configured tocommunicate with receivers (e.g., receivers 106 of FIG. 1) directly orindirectly through tags 102 or other transmitters. For example, in oneembodiment, a sensor 203 may be connected, wired (e.g., perhaps throughwires sewn into a jersey or bodysuit undergarment) or wirelessly, totags 102 to provide sensor data (including, for example, audio data) totags 102, which is then transmitted to the receivers 106. In anotherembodiment, a plurality of sensors (not shown) may be connected to adedicated antenna or transmitter, perhaps positioned in the helmet,which may transmit sensor data to one or more receivers.

FIG. 2B illustrates a game official 206 wearing equipment havingattached tags 102, sensors 203, and audio sensors 105 in accordance withsome embodiments. In the depicted embodiment, tags 102 are attached tothe official's jersey 209 proximate opposite shoulders. Sensors 203(e.g., accelerometers) are positioned in wristbands worn on theofficial's wrists as shown. Audio sensors 105 are positioned on, affixedto, or embedded within the official whistle 207 and affixed to theofficial's jersey 209. Sensors 203 (including the audio sensors 105) maybe configured to communicate with receivers (e.g., receivers 106 ofFIG. 1) directly or indirectly through tags 102 or other transmitters asdiscussed above in connection with FIG. 2A.

As discussed in greater detail below, the positioning of sensors 203(here, accelerometers) proximate the wrists of the official may allowthe receiver processing and distribution system 110 to determineparticular motions, movements, or activities of the official 206 for usein determining events (e.g., winding of the game clock, first down,touchdown, or the like). The official 206 may also carry otherequipment, such as penalty flag 208, which may also have a tag 102 (andoptionally one or more sensors) attached to provide additional data tothe receiver processing and distribution system 110. For example, thereceiver processing and distribution system 110 may use tag locationdata from the penalty flag 208 to determine when the official is merelycarrying the penalty flag 208 versus when the official is using thepenalty flag 208 to indicate an event, such as a penalty (e.g., bythrowing the penalty flag 208).

As discussed in further detail below, the positioning of audio sensors105 proximate the official whistle 207 and jersey 209 may allow thereceiver processing and distributing system 110 to determine particularsounds (e.g., audio data) produced by or near the official (whistlesounds, official calls, player sounds, inter-official communication, orthe like).

FIG. 2C illustrates an example of a ball 210 having tags 102 attached orembedded in accordance with some embodiments. Additionally, sensors 203may be attached to or embedded in the ball 210, such as accelerometers,time-of-flight sensors, audio sensors, or the like. In some embodiments,the sensor 203 may be connected, wired or wirelessly, to tag 102 toprovide sensor data to tag 102 which is then transmitted to thereceivers 106. In some embodiments, the sensor 203 may transmit sensordata to receivers separately from the tag 102, such as described abovein connection with FIG. 2A.

As will be apparent to one of ordinary skill in the art in view of thisdisclosure, once the tags 102, sensors 203, and audio sensors 105 ofFIGS. 2A-C are positioned on participants, they may be correlated tosuch participants. For example, in some embodiments, unique tag orsensor identifiers (“unique IDs”) may be correlated to a participantprofile (e.g., John Smith—running back, Fred Johnson—line judgeofficial, or ID 027—one of several game balls, etc.) and stored to aremote database accessible to the performance analytics system asdiscussed in greater detail below. Each participant profile may furtherinclude or be correlated with a variety of data including, but notlimited to, biometric data (e.g., height, weight, health data, etc.),role data, team ID, performance statistics, and other data that may beapparent to one of skill in the art in view of the foregoingdescription.

In some embodiments, such participant profile or role data may bepre-defined and stored in association with the unique tag or sensoridentifiers. In other embodiments, the participant profile or role datamay also be “learned” by the system as a result of received tag orsensor data, formation data, play data, event data, and/or the like. Forexample, in some embodiments the system may determine that a tag orsensor is not correlated to a participant profile and may analyze datareceived from the tag and/or sensor to determine possible participantroles, etc., which may be ranked and then selected/confirmed by thesystem or by a user after being displayed by the system. In someembodiments, the system may determine possible participant roles (i.e.,participant role data) based on determined participant location data(e.g., movement patterns, alignment position, etc.).

In some embodiments, as described in greater detail below, theparticipant profile or role data may also be updated by the system(i.e., to produce a data set for the participant that is far more robustthan that established at initial registration) as a result of receivedtag or sensor data, formation data, play data, event data, and/or thelike. In some embodiments, the participant profile and/or role data maybe used in a performance analytics system to weight the actions of theparticipants during analysis to assist in qualifying what is occurring,such as in determining formations, plays, events, etc.

Tag ID and Sensor Data Transmission Architecture

FIGS. 3A, 3B, 3C, 3D, and 3E show block diagrams of various differentarchitectures that may be utilized in transmitting signals from one ormore tags and sensors to one or more receivers of a receiver processingand analytics system in accordance with embodiments of the invention. Insome embodiments, the depicted architectures may be used in connectionwith the receiver processing and analytics system 110 of FIG. 1. Morethan one of these architectures may be used together in a single system.

FIG. 3A shows a RF location tag 102, such as that shown in FIG. 1, whichmay be configured to transmit a tag signal to one or more receivers 106.The one or more receivers 106 may transmit a receiver signal to thereceiver hub/locate engine 108.

The depicted RF location tag 102 may generate or store a tag uniqueidentifier (“tag UID”) and/or tag data as shown. The tag data mayinclude useful information such as the installed firmware version, lasttag maintenance date, configuration information, and/or a tag-individualcorrelator. The tag-individual correlator may comprise data thatindicates that a monitored individual (e.g., participant) is associatedwith the RF location tag 102 (e.g., name, uniform number and team,biometric data, tag position on individual, i.e., right wrist). As willbe apparent to one of skill in the art in view of this disclosure, thetag-individual correlator may be stored to the RF location tag 102 whenthe tag is registered or otherwise associated with an individual. Whileshown as a separate field for illustration purposes, one of ordinaryskill in the art may readily appreciate that the tag-individualcorrelator may be part of any tag data or even omitted from the tag.

The tag signal transmitted from RF location tag 102 to receiver 106 mayinclude “blink data” as it is transmitted at selected intervals. This“blink rate” may be set by the tag designer or the system designer tomeet application requirements. In some embodiments, it is consistent forone or all tags; in some embodiments it may be data dependent. Blinkdata includes characteristics of the tag signal that allow the tagsignal to be recognized by the receiver 106 so the location of the RFlocation tag 102 may be determined by the locating system. Blink datamay also comprise one or more tag data packets. Such tag data packetsmay include any data from the tag 102 that is intended for transmissionsuch as, for example in the depicted embodiment, a tag UID, tag data,and a tag-individual correlator. In the case of TDOA systems, the blinkdata may be or include a specific pattern, code, or trigger that thereceiver 106 (or downstream receiver processing and analytics system)detects to identify that the transmission is from a RF location tag 102(e.g., a UWB tag).

The depicted receiver 106 receives the tag signal, which includes blinkdata and tag data packets as discussed above. In one embodiment, thereceiver 106 may pass the received tag signal directly to the receivehub/locate engine 108 as part of its receiver signal. In anotherembodiment, the receiver 106 could perform some basic processing on thereceived tag signal. For instance, the receiver could extract blink datafrom the tag signal and transmit the blink data to the receivehub/locate engine 108. The receiver could transmit a time measurement tothe receive hub/locate engine 108 such as a TOA measurement and/or aTDOA measurement. The time measurement could be based on a clock timegenerated or calculated in the receiver, it could be based on a receiveroffset value as explained at paragraph [0053] above, it could be basedon a system time, and/or it could be based on the time difference ofarrival between the tag signal of the RF location tag 102 and the tagsignal of a RF reference tag (e.g., tag 104 of FIG. 1). The receiver 106could additionally or alternatively determine a signal measurement fromthe tag signal (such as a received signal strength indication (RSSI), adirection of signal, signal polarity, or signal phase) and transmit thesignal measurement to the receive hub/locate engine 108.

FIG. 3B shows a RF location tag 202 and sensor 203, such as those wornon an individual's person as shown in FIG. 2, which may be configured totransmit tag signals and sensor signals, respectively, to one or morereceivers 106, 166. While the foregoing description refers only to asensor 203 for illustration and brevity purposes, it is noted that thedepicted sensor 203 may be an audio sensor 105. Said differently, thecommunication channels and procedures discussed in connection with FIGS.3A-3E are as applicable to audio sensors 105 (one example type ofsensor) as they are to sensors 203. The one or more receivers 106, 166may then transmit receiver signals to the receiver hub/locate engine108. One or more receivers 106, 166 may share physical components, suchas a housing or antenna.

The depicted RF location tag 202 may comprise a tag UID and tag data(such as a tag-individual correlator) and transmit a tag signalcomprising blink data as discussed in connection with FIG. 3A above. Thedepicted sensor 203 may generate and/or store a sensor UID, additionalstored sensor data (e.g. a sensor-individual correlator, sensor type,sensor firmware version, last maintenance date, the units in whichenvironmental measurements are transmitted, etc.), and environmentalmeasurements (e.g., audio data). The “additional stored sensor data” ofthe sensor 203 may include any data that is intended for transmission toa RF location tag 202, a reference tag (e.g., 104 of FIG. 1), a sensorreceiver, a receiver 106, and/or the receiver/hub locate engine 108.

The sensor-individual correlator may comprise data that indicates that amonitored individual is associated with the sensor 203 (e.g., name,uniform number and team, biometric data, sensor position on individual,i.e., right wrist). As will be apparent to one of skill in the art inview of this disclosure, the sensor-individual correlator may be storedto the sensor 203 when the sensor is registered or otherwise associatedwith an individual. While shown as a separate field for illustrationpurposes, one of ordinary skill in the art may readily appreciate thatthe sensor-individual correlator may be part of any additional storedsensor data or omitted from the sensor altogether.

Sensors such as sensor 203 that are structured according to embodimentsof the invention may sense or determine one or more environmentalconditions (e.g. temperature, pressure, pulse, heartbeat, audio data,rotation, velocity, acceleration, radiation, position, chemicalconcentration, voltage) and store or transmit “environmentalmeasurements” that are indicative of such conditions. To clarify, theterm “environmental measurements” includes measurements concerning theenvironment proximate the sensor including, without limitation, ambientinformation (e.g., temperature, position, humidity, sound, etc.) andinformation concerning an individual's health, fitness, operation,and/or performance. Environmental measurements may be stored ortransmitted in either analog or digital form and may be transmitted asindividual measurements, as a set of individual measurements, and/or assummary statistics. For example, temperature in degrees Celsius may betransmitted as {31}, or as {33, 32, 27, 22, 20, 23, 27, 30, 34, 31}, oras {27.9}. In some embodiments, the sensor-individual correlator couldbe determined at least in part from the environmental measurements.

In the depicted embodiment, RF location tag 202 transmits a tag signalto receiver 106 and sensor 203 transmits a sensor signal to sensorreceiver 166. The sensor signal may comprise one or more sensorinformation packets. Such sensor information packets may include anydata or information from the sensor 203 that is intended fortransmission such as, for example in the depicted embodiment, sensorUID, additional stored sensor data, sensor-individual correlator, andenvironmental measurements. A receiver signal from receiver 106 and asensor receiver signal from sensor receiver 166 may be transmitted viawired or wireless communication to receiver hub/locate engine 108 asshown.

FIG. 3C depicts a sensor 203 communicating through a RF location tag 202in accordance with various embodiments. In one embodiment, the sensor203 may be part of (i.e., reside in the same housing or assemblystructure) of the RF location tag 202. In another embodiment, the sensor203 may be distinct from (i.e., not resident in the same housing orassembly structure) the RF location tag 202 but configured tocommunicate wirelessly or via wired communication with the RF locationtag 202.

In one embodiment, the RF location tag 202, the sensor 203, or both, maygenerate and/or store a tag-sensor correlator that indicates anassociation between a RF location tag 202 and a sensor 203 (e.g., tagUID/sensor UID, distance from tag to sensor in a particular stance, setof sensors associated with a set of tags, sensor types associated with atag, etc.). In the depicted embodiment, both the RF location tag 202 andthe sensor 203 store the tag-sensor correlator.

In the depicted embodiment, sensor 203 transmits a sensor signal to RFlocation tag 202. The sensor signal may comprise one or more sensorinformation packets as discussed above. The sensor information packetsmay comprise the sensor UID, a sensor-individual correlator, additionalstored sensor data, the tag-sensor correlator, and/or the environmentalmeasurements. The RF location tag 202 may store some portion of, or allof, the sensor information packets locally and may package the sensorinformation packets into one or more tag data packets for transmissionto receiver 106 as part of a tag signal or simply pass them along aspart of its tag signal.

FIG. 3D illustrates an example communication structure for a referencetag 104 (e.g., reference tag 104 of FIG. 1), an RF location tag 202, asensor 203, and two receivers 106 in accordance with one embodiment. Thedepicted reference tag 104 is a RF location tag and thus may include tagdata, a tag UID, and is capable of transmitting tag data packets. Insome embodiments, the reference tag 104 may form part of a sensor andmay thus be capable of transmitting sensor information packets.

The depicted sensor 203 transmits a sensor signal to RF reference tag104. The RF reference tag 104 may store some portion or some or all ofthe sensor information packets locally and may package the sensorinformation packets into one or more tag data packets for transmissionto receiver 106 as part of a tag signal, or simply pass them along aspart of its tag signal.

As was described above in connection with FIG. 1, the receivers 106 ofFIG. 3D are configured to receive tag signals from the RF location tag202 and the reference tag 104. Each of these tag signals may includeblink data, which may comprise tag UIDs, tag data packets, and/or sensorinformation packets. The receivers 106 each transmit receiver signalsvia wired or wireless communication to the receiver hub/locate engine108 as shown.

FIG. 3E illustrates an example communication structure between an RFlocation tag 202, a plurality of receivers 106, and a variety of sensortypes including, without limitation, a sensor 203 (e.g., an audio sensor105), a diagnostic device 233, a triangulation positioner 243, aproximity positioner 253, and a proximity label 263 in accordance withvarious embodiments. In the depicted embodiment, none of the sensors203, 233, 243, 253 form part of an RF location tag 202 or reference tag104. However, each may comprise a sensor UID and additional storedsensor data. Each of the depicted sensors 203, 233, 243, 253 transmitssensor signals comprising sensor information packets.

In the depicted embodiment, receiver 106 is configured to receive a tagsignal from RF location tag 202 and a sensor signal directly from sensor203. In such embodiments, sensor 203 may be configured to communicate ina communication protocol that is common to RF location tag 202 as willbe apparent to one of ordinary skill in the art in view of thisdisclosure.

FIG. 3E depicts one type of sensor referred to herein as a “proximityinterrogator”. The proximity interrogator 223 can include circuitryoperative to generate a magnetic, electromagnetic, or other field thatis detectable by a RF location tag 202. While not shown in FIG. 3E, aproximity interrogator 223 may include a sensor UID and other tag andsensor derived data or information as discussed above.

In some embodiments, the proximity interrogator 223 is operative as aproximity communication device that can trigger a RF location tag 202(e.g., when the RF location tag 202 detects the field produced by theproximity interrogator 223) to transmit blink data under an alternateblink pattern or blink rate. The RF location tag can initiate apreprogrammed (and typically faster) blink rate to allow more locationpoints for tracking an individual. In some embodiments, the RF locationtag may not transmit a tag signal until triggered by the proximityinterrogator 223. In some embodiments the RF location tag 202 may betriggered when the RF location tag 202 moves near (e.g., withincommunication proximity to) a proximity interrogator 223. In someembodiments, the RF location tag may be triggered when the proximityinterrogator 223 moves near to the RF location tag 202.

In other embodiments, the RF location tag 202 may be triggered when abutton is pressed or a switch is activated on the proximity interrogator223 or on the RF location tag itself. For example, a proximityinterrogator 223 could be placed at the start line of a racetrack. Everytime a car passes the start line, a car-mounted RF location tag 202senses the signal from the proximity interrogator and is triggered totransmit a tag signal indicating that a lap has been completed. Asanother example, a proximity interrogator 223 could be placed at aGatorade cooler. Each time a player or other participant fills a cupfrom the cooler a participant-mounted RF location tag 202 senses thesignal from the proximity interrogator and is triggered to transmit atag signal indicating that Gatorade has been consumed. As anotherexample, a proximity interrogator 223 could be placed on a medical cart.When paramedics use the medical cart to pick up a participant (e.g., aplayer) and move him/her to the locker room, a participant-mounted RFlocation tag 202 senses the signal from the proximity interrogator andis triggered to transmit a tag signal indicating that they have beenremoved from the game. As explained, any of these post-triggered tagsignals may differ from pre-triggered tag signals in terms of any aspectof the analog and/or digital attributes of the transmitted tag signal.

FIG. 3E depicts another type of sensor that is generally not worn by anindividual but is referred to herein as a “diagnostic device”. However,like other sensors, diagnostic devices may measure one or moreenvironmental conditions and store corresponding environmentalmeasurements in analog or digital form.

While the depicted diagnostic device 233 is not worn by an individual,it may generate and store a sensor-individual correlator for associationwith environmental measurements taken in connection with a specificindividual. For example, in one embodiment, the diagnostic device 233may be a blood pressure meter that is configured to store asenvironmental measurements blood pressure data for various individuals.Each set of environmental measurements (e.g., blood pressure data) maybe stored and associated with a sensor-individual correlator.

The depicted diagnostic device 233 is configured to transmit a sensorsignal comprising sensor information packets to a sensor receiver 166.The sensor information packets may comprise one or more of the sensorUID, the additional stored data, the environmental measurements, and/orthe sensor-individual correlator as discussed above. The sensor receiver166 may associate some or all of the data from the sensor informationpackets with other stored data in the sensor receiver 166 or with datastored or received from other sensors, diagnostic devices, RF locationtags 102, or reference tags. The sensor receiver 166 transmits a sensorreceiver signal to a receiver hub/locate engine 108.

Another type of sensor shown in FIG. 3E is a triangulation positioner243. A “triangulation positioner” is a type of sensor that sensesposition. The depicted triangulation positioner 243 includes a sensorUID, additional stored sensor data, and environmental measurements asdiscussed above.

In some embodiments, a triangulation positioner (also known as a globalpositioning system (GPS) receiver) receives clock data transmitted byone or more geostationary satellites (a satellite in a known or knowableposition) and/or one or more ground based transmitters (also in known orknowable positions), compares the received clock data, and computes a“position calculation”. The position calculation may be included in oneor more sensor information packets as environmental measurements.

In another embodiment, a triangulation positioner comprises one or morecameras or image-analyzers that receive emitted or reflected light orheat, and then analyzes the received images to determine the location ofan individual or sensor. Although a triangulation positioner maytransmit data wirelessly, it is not a RF location tag because it doesnot transmit blink data or a tag signal that can be used by a receiverhub/locate engine 108 to calculate location. In contrast, atriangulation positioner senses position and computes a positioncalculation that may then be used as environmental measurements by thereceiver hub/locate engine 108.

In one embodiment, a triangulation positioner could be combined with aRF location tag or reference tag (not shown). In such embodiments, thetriangulation positioner could compute and transmit its positioncalculation via the RF location tag to one or more receivers. However,the receiver hub/locate engine would calculate tag location based on theblink data received as part of the tag signal and not based solely onthe position calculation. The position calculation would be consideredas environmental measurements and may be included in associated sensorinformation packets.

As will be apparent to one of ordinary skill in the art, positioncalculations (e.g., GPS receiver position calculations) are not asaccurate as the location calculations (e.g., UWB waveform based locationcalculations) performed by receiver hub/locate engines structured inaccordance with various embodiments of the invention. That is not to saythat position calculations may not be improved using known techniques.For example, a number of influences, including atmospheric conditions,can cause GPS accuracy to vary over time. One way to control this is touse a differential global positioning system (DGPS) comprising one or anetwork of stationary triangulation positioners that are placed in aknown position, and the coordinates of the known position are stored inmemory as additional stored sensor data. These triangulation positionersreceive clock data from geostationary satellites, determine a positioncalculation, and broadcast a difference between the position calculationand the stored coordinates. This DGPS correction signal can be used tocorrect for these influences and significantly reduce location estimateerror.

Another type of sensor shown in FIG. 3E is a proximity detector 253. A“proximity detector” is a type of sensor that senses identity within anarea (e.g., a local area) that is small with respect to the monitoredarea 100 of FIG. 1. Many different ways of sensing identity (e.g., aunique ID or other identifier for a sensed object or individual) wouldbe apparent to one of ordinary skill in the art in view of thisdisclosure including, without limitation, reading a linear bar code,reading a two-dimensional bar code, reading a near field communication(NFC) tag, reading a RFID tag such as a UHF tag, HF tag, or lowfrequency tag, an optical character recognition device, a biometricscanner, or a facial recognition system.

In some embodiments, a proximity detector senses an attribute of anindividual (or an individual's wristband, tag, label, card, badge,clothing, uniform, costume, phone, ticket, etc.). The identity sensed bya proximity detector may be stored locally at the proximity detector 253as shown and transmitted as environmental measurements via one or moresensor information packets to a sensor receiver 166.

In some embodiments, a proximity detector 253 may have a definedposition, which is often stationary, and may be associated with alocation in the monitored area 100 of FIG. 1. For example, a proximitydetector 253 could be located at a finish line of a race track, anentrance gate of a stadium, with a diagnostic device, at a goal line orgoal post of a football field, at a base or home plate of a baseballdiamond, or a similar fixed location. In such embodiments where theproximity detector is stationary, the position coordinates of theproximity detector and a sensor UID could be stored to a monitored areadatabase (not shown) that is accessible by one or more of the receivers106, 166, the receiver hub/locate engine 108, and/or other components ofthe receiver processing and analytics system 110. In embodiments wherethe proximity detector is movable, a position calculation could bedetermined with a triangulation positioner, or the proximity detectorcould be combined with a RF location tag and located by the receiverhub/locate engine 108. While shown as separate fields for illustrationpurposes in FIG. 3E, identify information and position calculation couldcomprise part of the additional stored sensor data, the environmentalmeasurements, or both.

In one embodiment, the proximity detector could be associated with areference tag (e.g., tag 104 of FIG. 1) whose position is recorded inthe monitored area database. In other embodiments, the proximitydetector is movable, such that it may be transported to where it isneeded. For example, a proximity detector 253 could be located on amedical cart, first down marker, a diagnostic device, goal post, orcarried by a paramedic or security guard. In an embodiment where theproximity detector 253 is movable it would typically be associated witha RF location tag or triangulation positioner so that location (for a RFlocation tag) or position (for a triangulation positioner) can bedetermined at the time identity is sensed.

In the embodiment where the proximity detector includes a RF locationtag, the receiver hub/locate engine 108 would locate the associated RFlocation tag, and the tag data/sensor data filter 112 would associatethe tag location data for the associated RF location tag as the positionof the proximity detector, while determining the identity of anassociated individual from any received sensor information packets. Inthe alternate embodiment where the proximity detector includes atriangulation positioner, the triangulation positioner would compute aposition calculation that could be stored as additional stored sensordata and/or environmental measurements, and transmitted as one or moresensor information packets. In one embodiment, sensor informationpackets for a proximity detector may include both sensed identityinformation and a position calculation.

Another type of sensor shown in FIG. 3E is a proximity label 263. Aproximity label has a fixed position and an identification code (e.g., asensor UID). The proximity label 263 may further comprise additionalstored sensor data as shown. The depicted proximity label 263 isconfigured to be read by proximity detector 253. In some embodiments,proximity detector 253 may be further configured to write information toproximity label 263.

A proximity label 263 may be a sticker, card, tag, passive RFID tag,active RFID tag, NFC tag, ticket, metal plate, electronic display,electronic paper, inked surface, sundial, or otherwise visible ormachine readable identification device as is known in the art. Thecoordinates of the position of the proximity label 263 are stored suchthat they are accessible to the receive hub/locate engine 108. Forexample, in one embodiment, the position coordinates of a proximitylabel 263 could be stored in a field database or monitored area databaseaccessible via a network, or stored locally as additional stored data inthe proximity detector 253.

In some embodiments, a position of the proximity label 263 is encodedinto the proximity label 263 itself. For example, coordinates of aposition of the proximity label 263 could be encoded into a passive RFIDtag that is placed in that position. As another example, the coordinatesof a position of the proximity label 263 could be encoded into a printedbarcode that is placed in that position. As another example, a proximitylabel 263 comprising a NFC tag could be encoded with the location “endzone”, and the NFC tag could be placed at or near an end zone at Bank ofAmerica stadium. In some embodiments, the stored coordinates of theproximity label 263 may be offset from the actual coordinates of theproximity label 263 by a known or determinable amount.

In one embodiment, a proximity label 263 such as an NFC tag may beencoded with a position. When a sensor such as a proximity detectorapproaches the NFC tag it may read the position, then transmit theposition in a sensor information packet to the sensor receiver 166′ andeventually to the receiver hub/locate engine 108. In another embodiment,a proximity label 263 such as a barcode label may be encoded with anidentification code. When a smartphone with a proximity detector (suchas a barcode imager) and a triangulation positioner (such as a GPS chip,GPS application, or similar device) approaches the barcode label it mayread the identification code from the barcode, determine a positioncalculation from received clock data, then transmit the identity and theposition calculation to sensor receiver 166′ and eventually to thereceiver hub/locate engine 106 as part of one or more sensor informationpackets.

In the depicted embodiment, triangulation positioner 243 and proximitydetector 253 are each configured to transmit sensor signals carryingsensor information packets to sensor receiver 166′. The depicted sensors243, 253, like any sensor discussed herein, may transmit sensor signalsvia wired or wireless communication protocols. For example, anyproprietary or standard wireless protocol (e.g., 802.11, Zigbee, ISO/IEC802.15.4, ISO/IEC 18000, IrDA, Bluetooth, CDMA, or any other protocol)could be used for the sensor signals. Alternatively or additionally, anystandard or proprietary wired communication protocol (e.g., Ethernet,Parallel, Serial, RS-232, RS-422, USB, Firewire, I²C, etc.) may be used.Similarly, sensor receiver 166′, and any receiver discussed herein, mayuse similar wired and wireless protocols to transmit receiver signals tothe receiver hub/locate engine.

In one embodiment, upon receiving sensor signals from the triangulationpositioner 243 and the proximity detector 253, the sensor receiver 166′may associate some or all of the data from the received sensorinformation packets with other data stored to the sensor receiver 166′,or with data stored or received from other sensors (e.g., sensor 203,audio sensor 105), diagnostic devices 233, RF location tags 102, or RFreference tags 104. Such associated data is referred to herein as“associated sensor data”. In the depicted embodiment, the sensorreceiver 166′ is configured to transmit some or all of the receivedsensor information packets and any associated sensor data to thereceiver hub/locate engine 108 at part of a sensor receiver signal.

In one embodiment, a smartphone comprising a proximity detector (such asa barcode imager) and a triangulation positioner (such as a GPS chip)may associate an identification code determined from a barcode with aposition calculation from received clock data as associated sensor dataand transmit a sensor information packet that includes such associatedsensor data to the receiver hub/locate engine 108. In anotherembodiment, the smartphone could transmit a first sensor informationpacket including the identification code and the smartphone's uniqueidentifier to another sensor receiver, the smartphone could transmit asecond sensor information packet including the position calculation andthe smartphone's unique identifier to the sensor receiver, and thesensor receiver could associate the position calculation with theidentification code based on the common smartphone unique identifier andtransmit such associated sensor data to the receiver hub/locate engine108. In another embodiment, the sensor receiver could determine a firsttime measurement associated with the first sensor information packet anda second time measurement associated with the second sensor informationpacket that, in conjunction with the sensor UID, could be used, by thereceiver hub/locate engine 108, to associate the first sensorinformation packet with the second sensor information packet.

In one embodiment, the receiver hub/locate engine 108 receives receiversignals from the receiver 106 and sensor receiver signals from thesensor receivers 166, 166′. In the depicted embodiment, receiver 106 mayreceive blink data from the RF location tag 102 and transmits to thereceiver hub/locate engine 108 some or all of the blink data, perhapswith additional time measurements or signal measurements. In someembodiments, time measurements or signal measurements may be based on atag signal received from a RF reference tag (e.g., reference tag 104 ofFIG. 1). The receiver hub/locate engine 108 collects the blink data,time measurements (e.g., time of arrival, time difference of arrival,phase), and/or signal measurements (e.g., signal strength, signaldirection, signal polarization, signal phase) from the receivers 106 andcomputes tag location data for the tags 102 as discussed above inconnection with FIG. 1. In some embodiments, the receivers 106 may beconfigured with appropriate RF filters, such as to filter outpotentially interfering signals or reflections proximate the field ofplay or other area to be monitored.

The receiver hub/locate engine 108 may also access stored data or clockdata from local storage and from a network location. The receiverhub/locate engine 108 uses this information to determine tag locationdata for each RF location tag. It may also associate data derived orextracted from tag signals transmitted from one or more RF location tagswith information or data derived or extracted from sensor signalstransmitted from one or more sensors.

In addition to the TOA or TDOA systems previously described, otherreal-time location systems (RTLS) such as received signal strengthindication based systems could potentially be implemented by a receiverhub/locate engine 108. Any RTLS system using RF location tags, includingthose described herein, could require considerable processing by thereceiver hub/locate engine 108 to determine the tag location data fromthe blink data received from the tags. These may require timemeasurement and/or signal measurement in addition to blink data, whichpreferably includes a tag UID. In contrast, in other systems, such asglobal position systems (GPS) systems, location data is determined basedupon the position calculation transmitted from a GPS transmitter (alsoreferred to as a GPS receiver or GPS tag) which includes calculatedinformation about the location where the tag was positioned (i.e.,coordinates determined at the tag via satellite signal triangulation,etc.) when the position calculation was determined or stored. Thus, GPSinformation typically refers to additional information that istransmitted along with a GPS transmitter ID before the transmission isreceived by a sensor receiver.

A GPS host device or back-end server may receive the GPS information andsimply parse the position calculation (as opposed to calculating theposition information at the host device) and the GPS transmitter ID intoa data record. This data record may be used as a GPS positioncalculation, or it could be converted to a different coordinate systemto be used as a GPS position calculation, or it could be processedfurther with DGPS information to be used as a GPS position calculation.

Returning to FIG. 3C, the depicted RF location tag 202 is used to convey(sometimes called backhaul) sensor information packets to a receiver106. In some embodiments, while not shown, multiple sensors 203 maytransmit sensor signals carrying sensor information packets to RFlocation tag 202. Such received sensor information packets may beassociated with blink data that is transmitted to receiver 106.

In one embodiment, the receiver hub/locate engine 108 may parse sensorinformation packets from received tag data packets and associate suchsensor information packets with the RF location tag 202 that transmittedthe sensor information packet. Thus, the receiver hub/locate engine 108may be able to determine tag location data, which may comprise alocation and other data (e.g., tag data, tag UID, tag-individualcorrelator, sensor-individual correlator, additional stored sensor data,environmental measurements (e.g., audio data), tag-sensor correlator,identity information, position calculation, etc.) from one or more tagsor sensors. Such data and information may be transmitted to the receiverprocessing and analytics system 110.

In some embodiments, once the receiver hub/locate engine 108 determinesa location estimate of a RF location tag 102 at the time epoch of thetag signal, the receiver hub/locate engine 108 can also associate alocation estimate with the tag data packet included in the blink data ofsuch tag signal. In some embodiments, the location estimate of the tagsignal may be used as tag location data for the tag data packet. In someembodiments a Geographical Information System (GIS) may be used by thereceive hub/locate engine 108 to refine a location estimate, or to map alocation estimate in one coordinate system to a location estimate in adifferent coordinate system, to provide a location estimate for the tagdata packet.

In one embodiment, the location estimated for the tag data packet may beassociated with any data in the tag data packet, including a tag UID,other tag data, and, if included, one or more sensor informationpackets, including sensor UID, additional stored sensor data, andenvironmental measurements. Since environmental measurements may includea position calculation from a triangulation positioner (e.g., a GPSdevice), the receiver hub/locate engine 108 could parse the positioncalculation and use it to refine a location estimate for the tag datapacket.

Preferably, the receiver hub/locate engine 108 may access an individualdatabase to determine tag-individual correlators or sensor-individualcorrelators. Individual data (e.g., an individual profile) may be storedin a server, in tag memory, in sensor memory, or in other storageaccessible via a network or communication system, including tag data oradditional stored sensor data as explained previously.

In some embodiments, by comparing data accessed using asensor-individual correlator, the receiver hub/locate engine 108 mayassociate an individual with a sensor information packet received from asensor, and/or may associate an individual with such sensor. Because thereceiver hub/locate engine 108 may associate a sensor position estimatewith a sensor information packet, the receiver hub/locate engine 108 mayalso estimate an individual position for the associated individual.

In another embodiment, by comparing data accessed using a tag-sensorcorrelator, the receiver hub/locate engine 108 may associate a sensorwith a tag data packet received from a RF location tag 102. Because thereceiver hub/locate engine 108 may associate a location estimate with atag data packet, the receiver hub/locate engine 108 may also create asensor location estimate for the associated sensor. By comparing alocation estimate for a RF location tag with a sensor location estimateor a sensor position estimate, the receiver hub/locate engine 108 mayassociate a RF location tag with a sensor, or may associate a tag datapacket with a sensor information packet. The receiver hub/locate engine108 could also determine a new or refined tag-sensor correlator based onthis association.

In still another embodiment, by comparing a location estimate for a RFlocation tag with an individual location estimate or an individualposition estimate, the receiver hub/locate engine 108 may associate a RFlocation tag with an individual, or may associate a tag data packet withan individual. The receiver hub/locate engine 108 could also determine anew or refined tag-individual correlator based on this association.

In one embodiment, by comparing a location estimate for a sensor with anindividual location estimate or an individual position estimate, thereceiver hub/locate engine 108 may associate a sensor with anindividual, or may associate a sensor information packet with anindividual. The receiver hub/locate engine 108 could also determine anew or refined sensor-individual correlator based on this association.

Data derived or extracted from tag signals transmitted from one or moreRF location tags is referred to herein as “tag derived data” and shallinclude, without limitation, tag data, tag UID, tag-individualcorrelator, tag-sensor correlator, tag data packets, blink data, timemeasurements (e.g. time of arrival, time difference of arrival, phase),signal measurements (e. g., signal strength, signal direction, signalpolarization, signal phase) and tag location data (e.g., including taglocation estimates). Tag derived data is not derived by the RF locationtag, but rather, is derived from information transmitted by the RFlocation tag. Information or data derived or extracted from sensorsignals transmitted from one or more sensors is referred to herein as“sensor derived data” and shall include, without limitation, sensor UID,additional stored sensor data, sensor-individual correlator,environmental measurements, sensor information packets, positioncalculations (including sensor position estimates), positioninformation, identity information, tag-sensor correlator, and associatedsensor data. Information or data derived or extracted from audio sensorsignals transmitted by one or more audio sensors is referred to hereinas “audio data” and shall include without limitation, audio sensor UID,additional stored audio sensor data, audio sensor-individual correlator,audio sensor information packets, tag-audio sensor correlator, andassociated audio sensor data. Data derived or extracted from storedindividual data is referred to herein as “individual profileinformation”, “participant profile information”, or simply “profileinformation” and shall include, without limitation tag-individualcorrelator, sensor-individual correlator, identity information, name,uniform number and team, biometric data, tag position on individual. Invarious embodiments, the receiver hub/locate engine 108 may transmit tagderived data, sensor derived data, individual profile information,various combinations thereof, and/or any information from the GIS, thefield database, the monitored area database, and the individual databaseto the receiver processing and analytics system 110.

Example Receiver Hub and Receiver Processing and Distribution System

FIG. 4 illustrates an exemplary system 300 for providing performanceanalytics in accordance with some embodiments of the present invention.The depicted performance analytics system 300 may be distributed in areceiver hub 108 and a receiver processing and distribution system 110of the type depicted in FIG. 1. For example, locate engine 302 may bepart of the receiver hub 108 with the tag ID/Filter 304 through eventengine 322 forming part of the receiver processing and distributionsystem 110. In alternative embodiments, the performance analytics system300 may be housed or located in a single housing or unit. In still otherembodiments, the performance analytics system 300 may be distributedamong multiple additional housings or units depending upon theapplication and other design parameters that will be apparent to one ofordinary skill in the art in view of this disclosure.

The performance analytics system 300 of FIG. 4 may include a pluralityof tags 102, sensors 203, and/or audio sensors 105, associated withparticipants (e.g., players, officials, balls, field markers, etc.), aplurality of receivers 106 positioned within a monitored environment, areceiver hub/locate engine 302, one or more filters 304, a plurality ofdatabases, a plurality of processing engines, and a plurality of outputsystems. For illustration purposes, FIG. 4 depicts audio sensor 105separately from sensor 203; however, as discussed above, audio sensor105 is simply one type of sensor 203.

While only one type of receiver 106 is shown in FIG. 4, other types ofreceivers, e.g., sensor receivers 166, 166′ of FIG. 3E, may be used inaccordance with the embodiments discussed herein. The one or moredatabases may include databases for tag identifiers 354, player roles308, player dynamics or kinetics models 310, GIS data or a GIS database313, field data or a field knowledge database 314, formation models 316,play models 320, audio profiles 321, event models 323, official roles326, official models 328, ball models 332, weather data 375, or thelike. The plurality of processing engines may include a player dynamicsengine 306, a team formation engine 312, a play engine 318, an eventengine 322, an official dynamics engine 324, a field marker engine 334,a ball engine 330, and a model generation engine 338, or the like. Thesystem 300 may further include a game clock 380 and a universal clock385.

In an exemplary performance analytics system 300, such as illustrated inFIG. 4, the plurality of tags 102 (and sensors 203 and/or audio sensors105) may be attached to a participant and fixed locations as discussedin connection with FIGS. 2A-C. In some embodiments, the plurality oftags 102, sensors 203, and/or audio sensors 105 may be activated anddeactivated as needed, such as before and after a game or when damagedor to replace batteries, power suppliers, local memory, etc. Each of thetags 102 may transmit a tag signal, which may include tag derived data,which is received by one or more of the receivers 106. In someembodiments, the receivers 106 may be configured with appropriate RFfilters, such as to filter out potentially interfering signals orreflections proximate the field of play or other environment to bemonitored.

Each of the receivers 106 may receive tag derived data from the tags 102and transmit the tag derived data to the receiver hub/locate engine 302.The receiver hub/locate engine 302 collects the tag derived data fromthe receivers 106 and computes tag location data (based on the blinkdata) for the tags 102 as discussed above in connection with FIG. 1.

In the depicted embodiment, each of the receivers 106 receives sensorderived data from sensor signals transmitted by sensors 203 and audiodata from audio sensor signals transmitted by audio sensors 105. Inother embodiments, sensor receivers (e.g., sensor receivers 166, 166′ ofFIG. 3E) may transmit sensor signals comprising sensor derived data andaudio signals comprising audio data to the receiver hub/locate engine302.

The tag location data, tag derived data, sensor derived data (includingaudio data) may be provided from the receiver hub/locate engine 302 to atag ID/filter 304 that determines the type of participant associatedwith each received unique tag ID (and/or sensor ID) and routes theassociated tag derived data (and optionally, other received tag/sensorderived/audio data) to one or more engines associated with suchparticipant type (e.g., player, ball, official, field marker, etc.). Inone embodiment, the tag ID/filter 304 performs this routing, at least inpart, by correlating the received unique tag ID (and/or sensor ID) toprofile data or prior correlations (i.e., tag ID No. 0047 is correlatedto participant John Smith—quarterback, sensor ID No. 12459 is correlatedto Marcus Henderson—official, etc.) that may be stored to a tag/sensoridentification database 354 (i.e., tag-individual correlators,sensor-individual correlators, tag-sensor correlators, etc.). In someembodiments, the receivers 106 may also receive sensor derived data forother sensors 203, such as through the tags 102 or through separatetransmission means.

In one embodiment, perhaps in connection with the player illustration ofFIG. 2A, the tag ID/filter 304 identifies tag location data associatedwith a player and thus routes such data to a player dynamics engine 306for further processing. The player dynamics engine 306 is disposed incommunication with a player role database 308, which comprises playerrole data correlating tag and sensor UIDs to player profiles (e.g.,individual profile information) including, without limitation, whichroles (e.g., quarterback, running back, flanker, slot receiver, tightend, left tackle, left guard, center, right guard, right tackle,defensive end, defensive tackle, nose tackle, inside linebacker, outsidelinebacker, free safety, strong safety, cornerback kicker, punter, etc.)the players perform during a game.

The player dynamics engine 306 may also be disposed in communicationwith a dynamics/kinetics model database 310. The player dynamics engine306 may compare the tag location data, other tag and sensor deriveddata, and player role data to player dynamics/kinetics models todetermine aspects of the player dynamics or movement kinetics. Thedynamics/kinetics model database 310 may comprise models of differentaspects or dimensions that may be based on past player location data orother data generated by the model generation engine 338 as discussedbelow. The models may include, without limitation, models for aparticular player profile (e.g., John Smith), a player type (e.g.,quarterback), a player type for a particular team (e.g., a quarterbackfrom the Chicago Wizards), a player type for a particular formation(e.g., a quarterback in a spread offense), and the like. Such models mayconsider all three dimensions (x, y, z) of the tag location data foreach tag (e.g., 102 of FIG. 2A) and may further consider different tagposition arrays (e.g., two tag implementations—one proximate eachshoulder as in FIG. 2A, eleven tag implementations—one proximate eachshoulder, one proximate each elbow, one proximate each hand, oneproximate each knee, one proximate each foot, and one proximate thehead).

In one embodiment, the player dynamics engine 306 determines amulti-dimensional player location per unit time (e.g., participantlocation data) for each player based on the tag location data, other tagand sensor derived data, the player role data, and the playerdynamics/kinetics models. Such multi-dimensional player location mayinclude relative location of the player relative to the field of play,and/or general orientation of the player (e.g., standing, squatting,laying the ground, sitting, etc.) such as by correlating location dataand other tag and sensor derived data.

The player dynamics engine 306 uses the real time tag location datastream from the locate engine 302, as well as the player role database308 to provide accurate information about what a particular player isdoing in real time (or near real time). The player dynamics engine 306may further use other tag and sensor derived data, received from thelocate engine 302 in the depicted embodiment, to aid in determining notonly where the player is, but also how that player's location ischanging with time, velocity, acceleration, deceleration, orientation,or the like. The player dynamics engine 306 outputs multi-dimensionalplayer location information per unit time (e.g., participant locationdata).

In one embodiment, sensor derived data may comprise accelerometer datathat may indicate that a player (or portion of a player) is acceleratingor decelerating. In addition to the variety of other uses that will beapparent to one of ordinary skill in the art in view of this disclosure,the accelerometer data may be used to improve location accuracy for thesystem. For example, in circumstances where the real time tag locationdata stream erroneously suggests (perhaps due to interference, multipatheffects, signal reflections, signal losses due to line-of-sightblockages, etc.) that one of the possible locations for the player is 10feet away from a prior location, the accelerometer data could be used toconfirm that the player (or accelerometer affixed portion of the player)did not experience an acceleration sufficient to move that distance inthe amount of time provided.

In some embodiments, sensor derived data may comprise time-of-flightsensor data, which may indicate distances between participants (e.g.,distance of a player to other players, officials, the ball, etc.) orother objects. In applications involving complex tagged object movementssuch as, the example football application discussed herein,time-of-flight sensor data may be used to enhance the location accuracyof the system especially in circumstances where one or more tags orsensors are temporally unable to effectively transmit their data to oneor more receivers. For example, in one embodiment, a tag positionedwithin the ball may appear to the system as not moving because therunning back carrying the ball has run into a group of other players andthe bodies of such other players are actually blocking the line-of-sighttransmissions of the ball tag. In this embodiment, time-of-flightsensors positioned on the group of other players may be repeatedlydetermining and transmitting to one or more receivers the relativedistance between such time-of-flight sensors and the ball or ballcarrier. In this regard, the system may determine that the ball is nolonger at the ten yard line (i.e., the point where the system lastreceived a transmission directly from the ball tag) but rather hasadvanced toward the opponent's end zone to the six yard line. This andother similar techniques may be used alone or in combination with othertag and sensor derived data (e.g., accelerometer data, etc.) to create atype of mesh network that may adapt to temporary or sustainedline-of-sight blockages and improve the accuracy of locationdeterminations, formation determinations, play determinations, etc.

In some embodiments, the player dynamics engine 306 outputsmulti-dimensional player location information per unit time to an eventengine 322. In some embodiments, the multi-dimensional player locationinformation may include a ranked or weighted list of probable playerlocations while, in other embodiments, the multi-dimensional playerlocation information includes only a top, or most probable, playerlocation. This information may be used by the event engine 322 todetermine a number of important player events. For example, themulti-dimensional player location information may be used to indicatethat a player was tackled (i.e., experienced a rapid deceleration andtransited from a standing to a laying position) and is subsequentlylimping (e.g., tag and/or sensor data from tags/sensors proximate theplayers feet indicate a change in the gait of the player). In suchexample, the event engine 322 may be configured to transmit an alert(e.g., via text message, email, or the like) to an athletic trainer tohave the player checked-out or treated.

The player dynamics engine 306 may further output the multi-dimensionalplayer location information per unit time (e.g., participant locationdata) to a team formation engine 312. The team formation engine 312 isdisposed in communication with a formation models database 316 thatcontains models of various formations (e.g., offensive formations,defensive formations, special teams formations, etc.) defined for therelevant sport or activity (e.g., football in the depicted embodiment).The models of various formations may be derived from multi-dimensionalplayer location information collected during prior games, practices,etc., (e.g., learned by the system) or as input by one or more teams,such as by using model generation engine 338, historical data store 336,and/or team analytics engine 346.

The team formation engine 312 is further disposed in communication witha field data database 314 to assist in determining the likely teamformations. The field data database 314 may comprise, withoutlimitation, survey data for the field (e.g., various distances orcoordinates from reference tag(s) or other marker to yard lines, endzones, goal posts, boundaries, benches, locker rooms, spectator areas,other zones of interest, etc.).

In one embodiment, the team formation engine 312 determines one or moreformations (e.g., a probable formation or a ranked or weighted list ofprobable formations) based at least in part on the field data, themulti-dimensional player location information (which may include the tagderived data and/or sensor derived data), and the formation models. Theteam formation engine 312 may hypothesize the received multi-dimensionalplayer location data against models of every known formation todetermine a probable formation or a ranked or weighted list of probableformations. The team formation engine 312 is thus configured todetermine and output a data stream of formations versus time, whichconsiders how various formations change and may be used by downstreamengines to determine various events including the occurrence of a play.

In one embodiment, the team formation engine 312 may assign weights tothe received multi-dimensional player location data (i.e., participantlocation data), other types of tag derived data and/or sensor deriveddata, and/or to the formation models when determining a specificformation or ranked list of probable formations. For example, in oneembodiment, the team formation engine 312 may be configured to assign agreater weight to the position of the ball (which should remainstationary for a period of time as formations are being established,i.e., at the beginning of a play) than to the position of an official(which may move to some degree as formations are forming). In anotherembodiment, the team formation engine 312 may be configured to assign agreater weight to the location of the tight-end (which may indicate thestrong side of a formation) than to the location of a left guard (whoselocation seldom effects formation determination). In still anotherembodiment, the team formation engine 312 may be configured to assign agreater weight to sensor derived data associated with an accelerometerpositioned proximate an official's wrist (which may indicate winding ofthe play clock that often triggers the period during which formationsought to be forming) than to the location of any player.

In one embodiment, the team formation engine 312 outputs the data streamof formations versus time (e.g., formation data) to the play engine 318.The play engine 318 may also receive the output data stream (e.g.,multi-dimensional player location information versus time) from theplayer dynamics engine 306 and audio data from the tag ID/filter 304.The play engine 318 is disposed in communication with a play modelsdatabase 320 and an audio profile database 321. The play models database320 may include play models (e.g., known formation shifts or movementsover time). Such play models may be programmatically learned by thesystem (e.g., based on actual movements of players tracked by thesystem) or manually entered through an interface or other tool (e.g.,perhaps through the model generation engine 338). In this regard, theplay models database 320 may include historical plays executed by teams,potential/future plays from a team game plan or playbook, or otherhistorical data (e.g., from historical data store 336). The audioprofile database 321 may include audio profiles (e.g. known audiopatterns over time). Such audio profiles may be programmatically learnedby the system (e.g. based on actual audio data tracked by the system) ormanually entered through an interface or other tool (e.g. perhaps themodel generation engine 338). In this regard the audio profile database321 may include audio profiles of historical plays

In one embodiment, the play engine 318 may compare the audio datareceived from the location engine 302 and the audio profiles todetermine whether a play is forming, a play has started, a play is inprogress or a play has ended. For example the play engine 318 maydetermine that a play is forming based on audio profiles which mayinclude a play calls players talking, or the like. The play engine 318may thereafter determine play has started based on audio profiles whichmay include an official whistle, a quarterback cadence, a silence (e.g.ambient noise) followed by a snap, or the like. The play engine 318 maydetermine a play is in progress based on audio profiles which mayinclude impact noises, shouting, or the like. The play engine 318 maydetermine that a play has ended based on audio profiles which mayinclude an official whistle, official call, or the like.

In one embodiment, the play engine 318 may take the formations versustime data stream from the formation engine 312, the play models, and theplayer dynamics data stream (which may include tag location data and/orother tag and sensor derived data) to determine whether a play isforming, a play has started, a play is in progress, or a play has ended.For example, the play engine 318 may determine that it is most likelythat a pre-snap formation at the line of scrimmage has occurred (e.g.,an offensive team has aligned in a “pro set” formation and a defensiveteam has aligned in a “3-4” formation) indicating a play is about tobegin. The play engine 318 may thereafter determine that the offensiveand defensive players have begun rapidly accelerating towards and acrossa line of scrimmage thereby indicating that a play has begun. The playengine may further determine that an offensive player has been tackledby a defensive player thereby indicating that a play has concluded.

The play engine 318 may improve the event determination by additionallyanalyzing for audio data and audio profiles, alone or in conjunctionwith, the player models and player dynamic data stream. The play engine318 may determine an event such as the play start and stop times basedon audio data alone, by comparing the audio data to an audio profile andassigning a probability value. In an instance in which the probabilityvalue satisfies a predetermined threshold an event is determined. Forexample, an 80 percent probability of an official whistle, indicatingplay start, quarterback cadence and impact noises indicating play inprogress, and an official whistle to indicate the play has concluded,may satisfy a predetermined threshold of 75 percent.

In another embodiment, the play engine 318 may synchronize the timestamps of the audio data and the location data for analysis. The playengine 318 may compare the audio data to audio profiles correlated toplay models and assign a probability to each compared audio profile andplay model. If the probability value satisfies a predetermined value anevent may be determined. For example, the play engine 318 may determinein an instance in which the players move toward the line of scrimmage inconjunction with play calls and player talking. The play engine 318 maydetermine in an instance in which it is most likely that a pre-snapformation at the line of scrimmage has occurred (e.g., an offensive teamhas aligned in a “pro set” formation and a defensive team has aligned ina “3-4” formation) indicating a play is about to begin in conjunctionwith an official whistle and a quarterback cadence. The play engine 318may thereafter determine that the offensive and defensive players havebegun rapidly accelerating towards and across a line of scrimmage inconjunction with an official whistle and quarterback cadence therebyindicating that a play has begun. The play engine 318 may furtherdetermine that an offensive player has been tackled by a defensiveplayer in conjunction with impact noise and an official whistle therebyindicating that a play has concluded.

In some embodiments, the play engine 318 may use assigned weights (orassign weights) to the received data (e.g., the tag derived data, thesensor derived data, the multi-dimensional player location data, theformations data, officials data, etc.) for use in analyzing the data anddetermining the most probable play events. For example, the play engine318 may determine that data for particular participants (e.g., a leftguard) has a lower relevance for a particular formation (e.g., a pro setoffensive formation) and assign a lower weight to that data during theanalysis than to another participant (e.g., the ball, the quarterback, awide receiver, etc.).

In some embodiments, the play engine 318 is disposed in communicationwith an official dynamics engine 324 to further improve the playdetermination accuracy of the system. The official dynamics engine 324may provide data about the movements, actions, positions of an official,which the play engine 318 may use when determining a probable playand/or the status of a play. For example, as discussed in connectionwith FIG. 2B above, an official may be provided with wrist basedaccelerometers or other sensors (e.g., a whistle sensor), which may beused to flag the beginning or ending of a given play based on themovement or action of an official (e.g., rotating an arm to wind theplay clock, indicate touchdown with two arms raised, blow a whistle,etc.).

The play engine 318 may improve the play determination accuracy byanalyzing audio data in conjunction with official dynamics data. Thesensor data based on call movements, blowing a whistle, or throwing aflag may be analyzed with audio data correlating to an official whistle(removing discrepancies in an instance in which an official may placethe whistle in their mouth, but not blow or sound the whistle), anofficial call, or official to official discussions.

The play engine 318 may analyze how the team formations occur and howthey break up to determine both start and stop of a play (e.g., start ofplay event, end of play event, etc.). For example, the play engine 318may determine that offensive and defensive formations coalescedproximate a line of scrimmage and then broke up with various receiversheading towards the defensive team's end zone, there was all kinds ofactivity around a quarterback which eventually dissipated, and thatdefense players were tracking one of the receivers downfield until thereceiver crossed into the end zone and an official raised his arms. Theplay engine 318 may determine that this participant activity best fits aplay model whereby a ball was thrown and caught by a receiver who thenscored a touchdown thereby ending the play. The play engine 318 mayimprove the determination of a play by analyzing audio data which mayinclude crowd noise consistent with scoring, failing to score firstdown, or the like. For example, the crowd noise may be loud and excited(e.g. higher relative frequency) for scoring or first down and swell andthen fall for a failure to score a touchdown.

In some embodiments, the play engine 318 may hypothesize the receivedmulti-dimensional player location data (e.g., participant location data)and the data stream of formations versus time against models of everyknown play to determine a probable play or a ranked list of probableplays. The play engine 318 is thus configured to determine and output adata stream of plays versus time, which may be communicated to the eventengine 322.

In some embodiments, the tag ID/filter 304 may determine that receivedtag derived data and/or sensor derived data (including audio data)corresponds to one or more officials. Such official correlatedtag/sensor derived data is routed to the official dynamics engine 324.The official dynamics engine 324 is disposed in communication with anofficial roles database 326, which comprises official roles datacorrelating tag and sensor IDs (or other tag/sensor individualcorrelators) to official profiles including, without limitation, whichroles (e.g., referee, umpire, head linesman, line judge, back judge,field judge, side judge, etc.) the officials perform during a game.

The official dynamics engine 324 may also be disposed in communicationwith a dynamics/kinetics model database 328. The official dynamicsengine 324 may compare the tag location data, other tag/sensor deriveddata, and official role data to official dynamics/kinetics models todetermine aspects of the official dynamics or movement kinetics. Thedynamics/kinetics model database 328 may comprise models of differentaspects or dimensions that may be based on past official location dataor other data generated by the model generation engine 338 as discussedbelow. The models may include, without limitation, models for aparticular official profile (e.g., Ralph Stevens), an official type(e.g., referee), an official type for a particular formation (e.g., areferee positioned during a kickoff), and the like. Such models mayconsider all three dimensions (x, y, z) of the tag location data foreach tag (e.g., 102 of FIG. 2B) and may further consider different tagposition arrays (e.g., two tag implementations—one proximate eachshoulder as in FIG. 2B, eleven tag implementations—one proximate eachshoulder, one proximate each elbow, one proximate each hand, oneproximate each knee, one proximate each foot, and one proximate thehead).

In one embodiment, the official dynamics engine 324 determines amulti-dimensional official location per unit time for each officialbased on the tag location data, other tag and sensor derived data, theofficial role data, and the official dynamics/kinetics models. Suchmulti-dimensional official location may include (1) a relative locationof the official relative to the field of play, (2) a general orientationof the official (e.g., standing, squatting, laying the ground, sitting,etc.), and (3) a specific orientation of the official (e.g., armsraised, arms at hips, one hand grasping the wrist of the other arm,etc.).

The official dynamics engine 324 uses the real time tag location datastream from the locate engine 302, as well as the official role database326 to provide accurate information about what a particular official isdoing in real time (or near real time). The official dynamics engine 324may further use tag and sensor derived data, received from the locateengine 302 in the depicted embodiment, to aid in determining not onlywhere the official is, but also how that official's location is changingwith time, velocity, acceleration, deceleration, orientation, or thelike. For example, in one embodiment, the sensor derived data maycomprise accelerometer data that may indicate that an official (orportion of an official) is accelerating or decelerating. The officialdynamics engine 324 outputs multi-dimensional official locationinformation per unit time. Such multi-dimensional official locationinformation may include information regarding the official's location,how the location is changing with time, orientation of the official,motions or gestures of the official, or the like.

In some embodiments, the tag ID/filter 304 may determine that receivedtag and/or sensor derived data corresponds to the game ball (e.g., aball such as the ball shown in FIG. 2C, which is used or capable of usein the field of play). Such ball correlated tag/sensor derived data isrouted to the ball dynamics engine 330. While the ball engine 330 is notshown in communication with a “roles” database as in the case of some ofthe other processing engines, one of ordinary skill in the art willreadily appreciate some ball role data may be used, such as a ball ID orthe like, indicating that the received tag/sensor derived data isassociated with a given ball.

The ball engine 330 may access a ball models database 332, whichcomprises data indicating how location data and other tag and sensorderived data correlates to particular ball events during play. The ballengine 330 may provide information regarding the ball'sposition/location (vertical and/or horizontal), how the location ischanging with time, the velocity of the ball, the rotation of the ball,or the like for determining events during play. The ball engine 330 mayoutput ball data streams to the event engine 322. In some embodiments,although not shown in FIG. 3, the ball engine may also output a datastream to other processing engines for analysis, such as to the playengine 318 for use in determining status of a play.

In some embodiments, the tag ID/filter 304 may determine that receivedtag and/or sensor derived data corresponds to a field marker (e.g.,penalty flags, line of scrimmage markers, yards to gain markers, and thelike). The tag ID/filter 304 may then route such field marker correlatedtag/sensor derived data to a field marker engine 334 for furtherprocessing. The field marker engine 334 may provide informationregarding field marker location, how the location is changing with time,or the like, for determining events during play. The field marker engine334 may output data streams to the event engine 322. In someembodiments, although not shown in FIG. 3, the field marker engine mayalso output a data stream to other processing engines for analysis, suchas to the play engine 318 for use in determining the status of a play orplay event.

In some embodiments, a game clock 380 may be provided that is configuredto keep an official time for a game or other tracked activity. Inapplications such as the depicted football application, the game clockis configured to count down from some standard period or quarter length(e.g., 15 minutes) and may be periodically stopped or started by one ormore officials and/or the game operations system 342 as discussed ingreater detailed below. While not separately illustrated in FIG. 3, thegame clock 380 may further include a play clock, shot clock, pitchclock, or the like, which depending upon the application, may also bestarted and stopped by one or more officials and/or the game operationssystem 342.

The universal clock 385 provides a system time for the performance andanalytics system 300. As will be apparent to one of ordinary skill inthe art in view of this disclosure, the universal clock 385 is runningclock for tracking, cataloging, and calibrating system actions,processes, and events. For illustrations purposes, the game clock 380and the universal clock are shown as inputs for the event engine 322;however, in other embodiments, such clocks may provide inputs to any orall of the player dynamics engine 306, the team formation engine 312,the play engine 318, the event engine 322, the official dynamics engine324, the field marker engine 334, the ball engine 330, and the modelgeneration engine 338.

An event engine 322 may receive the outputs from the player dynamicsengine 306, the team formation engine 312, the play engine 318, theaudio profile database 321, the event model database 323, the officialdynamics engine 324, the ball engine 330, the field marker engine 334,the weather data store 375, a game clock 380, and a universal clock 385to determine events occurring during game play or to perform analytics,including predictive analytics, on game related data. In someembodiments, the event engine 322 determines such events and performssuch analytics by comparing its received inputs to a historic data store336 containing past events or analytics. In some embodiments, the eventengine 322 outputs event data streams (e.g., one or more output events)to a number of systems including, without limitation, a visualizationsystem 340, a game operations system 342, a camera control system 344, ateam analytics system 346, a league analytics system 348, a statisticssystem 350, an XML feed and/or instant message feed 352, a historicaldata store/engine 336, or other systems as may be apparent to one ofordinary skill in the art in view of this disclosure.

In some embodiments, the event engine 322 may use audio data todetermine if audio data correlates to an event audio profile. The eventengine 322 receives the audio data from the locate engine 302 and eventaudio profiles from the event model database 323 to determine if anevent has occurred. The event engine 322 may compare the audio data toaudio profiles and assign a probability to each compared audio profile.If the probability satisfies a predetermined value an event may bedetermined, or the event engine 322 may further compare participantlocation data to event models.

In an instance in which an event determination includes both audio dataand participant location data the event engine 322 may request theappropriate time stamp from the game clock 380 and universal clock 385.The universal and/or game time is associated with the audio data andparticipant location data. The play engine may further cross check theuniversal/game time stamp with time stamp from the tag locate engine 302to increase accuracy. The event engine 322 may synchronize theparticipant location data and the audio data. Once synchronized theevent engine 322 analyzes the participant location data in conjunctionwith the event audio profile to determine an event.

The event engine 322 may compare the selected event audio profile,player dynamics, team formations, play models, official dynamics, balldata, field marker data, weather data, audio data, or the like to eventmodels and assigns a probability value to each compared event model andassign a probability value. The event model may include, but not limitedto participants on field, roles of participants, locations ofparticipants, formations of participants, audio profile, field markerlocation, or the like. If the event model probability satisfies thepredetermined value an event is generated. A event may comprise startand end time of the play, duration of the event, number and specificparticipants of the event, formation and dynamic paths of the event,weather conditions, field positions and change to field positions foreach participant, downs, change in ball position, and the like.

In some embodiments the event engine outputs the event to a numbersystems including, without limitation, a visualization system 340, agame operations system 342, a camera control system 344, a teamanalytics system 346, a league analytics system 348, a statistics system350, an XML feed and/or instant message feed 352, a historical datastore/engine 336, or other systems as may be apparent to one of ordinaryskill in the art in view of this disclosure.

In some embodiments, the event engine 322 may output event data streamsthat include the time delay between tag and/or sensor transmissions andthe determination of the events such that other processes, such as avisualization system, game operations system, etc., may properlycorrelate to different inputs (e.g., video recording versus thedetermined events) so that the different inputs are synchronized. Inother embodiments, the event data streams may include time stamps (gametime stamp, universal time stamp, etc.) for determined events or othersystem processes. In this way, the performance and analytics system 300or some downstream system can determine, inter alia, which events orprocesses occurred in-game (i.e., during a running game or play clock)or out-of-game (i.e., while the game or play clock were stopped).

In various embodiments, the event data streams or output events providedby the event engine may include tag events (e.g., battery lowindication, error indication, etc.), sensor events (e.g., battery lowindication, error indication, etc.), locate engine events (e.g., statusindications, error indications), tag ID/Filter events (e.g., statusindications, error indications), player dynamics engine events (e.g.,status indications, error indications), player events (e.g., playertackled indication, player injured indication, etc.), official dynamicsengine events (e.g., status indications, error indications), officialevents (e.g., official injured indication, etc.), ball engine events(e.g., status indications, error indications), ball events (e.g., newball required indication, etc.), team formation engine events (e.g.,status indications, error indications), team formation events (e.g.,formation type indication, new formation indication, illegal formationindication, etc.), play engine events (e.g., status indications, errorindications), play events (e.g., play type indications such as run,pass, punt, field goal, etc., play results, and in-play or sub-playevents such as bootleg, 3 step drop, 5 step drop, 7 step drop, crossingpattern, hook pattern, fly pattern, drive block, pass block, spin move,swim move, press coverage, zone coverage, etc.), or any other eventsthat may be apparent to one of ordinary skill in the art in view of thisdisclosure. A variety of additional event data streams or output eventsare described in connection with the analytics systems and controlsystems discussed below.

In one embodiment, the event engine 322 outputs an event data stream tothe visualization system 340 that may be used by the visualizationsystem to provide enhanced telecasts or game experiences for televisionbroadcasts, streaming mobile device clients, and other media outlets,gaming systems, and other computer graphics visualization systems. Suchevent data streams may be used to provide enhanced graphics, displays,information, visualizations, and the like. For example, and withoutlimitation, the visualization system 340 may receive real time (or nearreal time) data including, without limitation, player ID, official ID,team ID, formation identifiers, play identifiers, pre-snap playprobabilities, play diagrams, player route data, player speed data,player acceleration data, ball route date, ball speed data, ballacceleration data, player trend information, offensive team trendinformation, defensive team trend information, special teams trendinformation, and other tag and/or sensor derived data. In someembodiments, the visualization system 340 may be configured to provide adynamically configurable interface that may be engaged by a user toselect graphics or areas of focus. For example, in one embodiment, auser may configure the system to display possible passing lanes for aquarterback to his eligible receivers. In still other embodiments, thevisualization system 340 may output a data stream for use in gamingsystems, such as plays, player actions, or the like.

In gaming systems examples, the visualization system 340 may provideoutput of event data that may be configured for display via a gamingconsole or handheld device. Such visualization system outputs may, forexample, provide for incorporating actual or predicted actions of a“live” player into a gaming environment. In some embodiments, thevisualization system may also access stored computer generated or usercreated avatars for use with the event data stream.

In one embodiment, the event engine 322 outputs an event data stream tothe game operations system 342 that may be used by the game operationssystem to coordinate, manage, or assist in the coordination or managingof game operations including, without limitation, the game clock 380(and optionally the play clock), down and distance determination, scoreboard operations, penalty enforcement, and the like. For example, andwithout limitation, the game operations system 342 may receive real time(or near real time) data from the event engine 322 including, withoutlimitation, a clock start indication, a clock stop indication, a playstart indication, a play end indication, a reset play clock indication,a 1^(st) down indication, a 2^(nd) down indication, a 3^(rd) downindication, a 4^(th) down indication, a turnover indication, a yard togain indication, a 5 yard penalty indication, a 10 yard penaltyindication, a 15 yard penalty indication, an end of quarter indication,an end of half indication, and an end of game indication.

Said differently, the event engine 322 may determine a number of eventsthat may be output to the game operations system or other devices. Suchevents may include, without limitation, a ready for play event (e.g., anofficial has spotted the ball at the line of scrimmage and started aplay clock in a football example, a pitcher has received the ball fromhis catcher in a baseball example, or the pins have been set in abowling example), a start of play event (e.g., the ball has been snappedin a football example, the pitcher has begun his pitching motion orwind-up in a baseball example, or a bowler has begun his bowling motionin a bowling example), and an end of play event (e.g., the official hasblown a whistle in a football example, an umpire has called a thirdstrike in a baseball example, or the nine pins have been knocked down ina bowling example). Such events may be used to determine plays,formations, and to output play diagrams (e.g., graphs or plots ofparticipant location versus time from a start of play event to an end ofplay event).

The event engine 322 may be further configured to output a play resultto the game operations system 342 or other device. Such play results mayinclude, for example and without limitation, a gain of twelve yards, aloss of three yards, an interception, a touchdown, live play, a firstand subsequent downs, and the like in football embodiments; a ball, astrike, a fly-out, a single, a double, a home run, a run scored, and thelike in baseball embodiments; and a gutter, a strike, a spare, and thelike in bowling embodiments.

As would be apparent to one of skill in the art, the various enginesand/or output systems may include security measures, such as encryption,access permissions, and the like, to secure system inputs and outputs.In some embodiments, the engines and/or output systems may comprisesecurity measures to prevent hacking, jamming, transmissioninterception, etc. to prevent interference from outside parties, such asthird parties attempting to gain information that may be advantageous inwagering, for example.

In one embodiment, the event engine 322 outputs an event data stream tothe camera control system 344 that may be used by the camera controlsystem to engage or transition engagement between one or moretelevision, film, or other cameras to capture game events. For example,and without limitation, the camera control system 344 may receive realtime (or near real time) data including, without limitation, an engageor disengage camera 1 indication, an engage or disengage camera 2indication, an engage or disengage camera 3, . . . and an engage ordisengage camera n indication. In some embodiments, the event engine 322may output camera control indications (e.g., event data) based on realtime (or near real time) game activity (e.g., ball location datasuggests that the ball is closest to a known field of view for camera 4and, thus, an engage camera 4 indication is transmitted to the cameracontrol system 344). In other embodiments, the event engine 322 mayoutput camera control indications (e.g., event data) based in part on aprediction of game activity (e.g., ball position, acceleration, anddirection data suggests that the ball has just left the quarterback'shand and is being passed along a direction and at a velocity indicativeof being caught in the field of view of camera 4 and, thus, an engagecamera 3 indication is transmitted to the camera control system 344). Inother embodiments, the camera control system 344 may provide indicationssuch as to tilt, pan, or zoom in connection with a particular camerabased on event data or predicted actions, or set a location or point ofview based on where a player, formation, or play may be best viewed.

In one embodiment, the event engine 322 outputs an event data stream tothe team analytics engine 346 that may be used to generate real time (ornear real time) analytics (e.g., player performance information,formation performance information, play performance information, andteam performance information) concerning game activity that may beuseful to individual teams. For example, in one embodiment, the teamanalytics engine 346 may use event data to determine actual gameperformance versus playbook design including, without limitation, anevaluation of player routes, offensive, defensive, and special teamsformations, offensive blocking protection schemes, defensive blitzingschemes, and the like.

In another embodiment, the team analytics engine 346 may use event datato determine actual game performance versus historical game performance(such as by using historical data store 336) including, withoutlimitation, an evaluation of game performance (e.g., player, team,offense, defense, special teams, etc.) versus prior year performance,prior game performance, prior quarter performance, prior possessionperformance, prior play performance, and the like. In this regard, aswill be apparent to one of ordinary skill in the art, the team analyticsengine 346 may be used to evaluate performance (e.g., the left tacklehas missed three assignments), identify trends (e.g., the defensive teamconsistently sends a linebacker blitz against a spread offensiveformation), make player substitutions (e.g., a second string left tacklehas performed better historically against the right end of the defenseand thus should be substituted for the starting left tackle), revisegame plans, or provide alerts (e.g., the flanker has experiencedsignificantly reduced speed, acceleration, and performance followingbeing tackled and thus an alert should be generated to the trainingstaff to ensure that such player is medically evaluated).

For example, in one embodiment, a trainer may have a device, such as ahandheld device, tablet, etc., that may receive alerts regarding aparticular player. The trainer may receive background information and/orpast information on a player as well as what change the system hasidentified to cause the alert, such as a change in gait, slower routerunning, etc. The trainer may then be able to evaluate the player andprovide input to the system regarding the player evaluation, such as iffurther attention is required or if the player can return to play. Insome embodiments, such alert and evaluation results may also be providedto the league analysis system, such as for use in determining injurytrends or the like.

In some embodiments, the team analytics engine 346 may be used to alerta team (e.g., coaches) to focus on specific players who are performingsub-par versus their normal (historical) performance, such as by playsor by teams. In some embodiments, the team analytics engine 346 mayfurther output analysis results to the historical data store 336 or thelike, for use in future analysis and/or the building or updating ofvarious models. The event engine 322 may send an event or alert to theteam analytics engine 346 in response to an audio data associated withan audio profile of a scream, yell of “medic”, other identifier ofplayer injury, or the like.

In another embodiment the team analytics engine may also correlate theaudio data to the location data for review of team analytics. Forexample, the quarterback may yell “bravo bravo” indicating that heanticipates the defense to blitz. The team could analyze when thequarterback call was made in relation to the positions of the defensiveplayers and the offensive players. Further, the team may be able toanalyze the change in position of the offensive and defense players inresponse to the quarterback call.

In one embodiment, the performance and analytics system is configured toevaluate player performance by correlating at least one tag to theplayer; receiving blink data (and other tag derived data) transmitted bythe at least one tag; determining tag location data based on the blinkdata; receiving player role data; comparing the tag location data toplayer dynamics/kinetics models based at least in part on the playerrole data; determining player location data based on the comparing thetag location data to the player dynamics/kinetics models; anddetermining player performance information based on comparing the playerlocation data to stored player location data. In some embodiments, thestored player location data may be stored to the historical data store336 and may include player location data for the actual player to beevaluated (e.g., Frank Smith, left tackle, #55) and/or player locationdata for another player (e.g., Fred Johnson, left tackle, #65) who playsa similar position to the actual player to be evaluated. In still otherembodiments, the stored player location data may include competitivedata based on the performance of the actual player against an opposingplayer (e.g., the left tackle blocked the right defense end successfullyin five prior match-ups, the defensive back caused a delay by the widereceiver of 2 seconds in running a passing route by applying presscoverage, etc.).

In another embodiment, the performance and analytics system isconfigured to evaluate official performance by correlating at least onetag to the official; receiving blink data (and other tag derived data)transmitted by the at least one tag; determining tag location data basedon the blink data; receiving official role data; comparing the taglocation data to official dynamics/kinetics models based at least inpart on the official role data; determining official location data basedon the comparing the tag location data to the official dynamics/kineticsmodels; and determining official performance information based oncomparing the official location data to stored official location data.In some embodiments, the stored official location data may be stored tothe historical data store 336 and may include official location data forthe actual official to be evaluated and/or official location data foranother official who held a similar position (e.g., referee, umpire,etc.) to the actual official to be evaluated.

In one embodiment, the event engine 322 outputs an event data stream tothe league analytics engine 348 that may be used to generate real time(or near real time) analytics concerning game activity that may beuseful to a league (i.e., a collection of teams). For example, in oneembodiment, the league analytics engine 348 may use event data toimprove game safety by identifying injury trends (e.g., playerconcussions occur at a higher rate when an offensive team runs crossingpassing routes from a spread formation against a 3-4 defense, etc.). Inanother embodiment, the league analytics engine 348 may use event datato evaluate rule changes (e.g., a rule change intended to speed up gameplay is or is not achieving its intended result). In still anotherembodiment, the league analytics engine 348 may use event data toimprove officiating (e.g., determining the accuracy of official calls).In some embodiments, the league analytics engine 348 may further outputanalysis results to the historical data store 336 or the like, for usein future analysis and/or the building or updating of various models.

In one embodiment, the event engine 322 outputs an event data stream tothe statistics engine 350 that may be used to generate real time (ornear real time) statistics concerning game activity. Such statistics mayinclude, without limitation, offensive statistics (e.g., passing,rushing, receiving, turnovers, touchdowns scored, etc.), defensivestatistics (e.g., tackles, sacks, interceptions, turnovers generated,etc.), special teams statistics (e.g., punt length, punt hang time,average return, long return, field goal accuracy, etc.), play diagrams,length of play statistics (e.g., 4.8 second average play, 22 secondaverage pre-snap formation period, etc.), player participationstatistics (e.g., John Smith participation in 42 of 68 offensive plays,etc.), summary statistics (e.g., top scorers, fantasy points, minutes onoffense, etc.), official statistics (e.g., penalties called, locationtracking diagrams per play, etc.) and the like. In some embodiments, thestatistics engine 350 may further output statistics and results to thehistorical data store 336 or the like, for use in future analysis and/orthe building or updating of various models.

In one embodiment, the event engine 322 outputs an event data stream tothe XML feed and/or instant messaging feed engine 352 that may be usedto generate XML or instant messaging data streams that may include livedata such as plays, scoring plays, other scoring info, results, topscorers, summary statistics, or the like.

In one embodiment, the event engine 322 may output an event stream thatmay be used to annotate or tag a game recording, for example, usingvisualization system 340, game operations system 342, or the like. Forexample, in one embodiment, the event engine 322 may flag, tag, orannotate certain events (e.g., plays, penalties, formations, clockstart/stop, etc.) into a video recording or live data stream of a gamefor later playback or analysis. In some embodiments, any eventidentified by the event engine 322 may be flagged, tagged, or annotatedto a video or other data stream to provide for ease of lateridentification. In this regard, various events may be readily searched,identified, stored to a database in an indexed way, and/or analyzed.

In some embodiments, the event engine 322 may determine events occurringproximate one or more play boundaries. For example, using outputs fromthe player dynamics engine 306, the ball engine 330, and the officialdynamics engine 324 the event engine 322 may determine that a touchdownhas been scored (i.e., a player has carried the ball across a goalboundary into the endzone). In particular, the event engine 322 maydetermine that a running back carried the ball (based on location datareceived from the ball engine and the player dynamics engine) across thegoal boundary (based on field data), which was confirmed by the nearestofficial signaling touchdown by raising both arms (based on locationdata received from the official dynamics engine). The event engine 322may improve the determination of an event by associating audio data tothe location data. For example, the touchdown being scored may correlatewith excited (e.g. relatively high frequency) crowd noise and a failureto score the touch down may correlate with a swell and fall of crowdnoise.

In some embodiments, the event engine 322 may output an event datastream to a historical data store/engine 336, which may store datagenerated by the various processing engines over time. The historicaldata store/engine 336 may be accessed by various systems, such as foruse in providing analytics or generating new models. For example,historical data store/engine 336 may provide historical data to modelgeneration engine 338, which the model generation engine 338 may use inlearning (or developing) new play or formation models that should beadded to the respective model databases. In some embodiments, thehistorical data store/engine 336 may be accessed by the analytics andstatistics systems to generate more in-depth analytics or statistics. Insome embodiments, the historical data store 336 may comprise prior eventand tag derived data received by the system for each individual player(e.g., John Smith) and may also comprise player data received from othersources, such as from manual input tools (i.e., such as using a form ortemplate) or external data sources (e.g., other statistics databases,etc.).

In some embodiments, the event engine 322 may output an event datastream that may be used in conjunction with historical results, such asfrom historical data store 336, for determining odds for outcomes ofvarious team matchups. For example, the event data stream and historicalevent data may be analyzed to generate and/or change predicted odds foroutcomes of each play, etc., which may be used in a wagering system orthe like.

In some embodiments, the team analytics system 346 may provide aninterface tool (i.e., perhaps through the model generation engine 338)configured to allow a team to input future plays (i.e., a game plan).Such future plays may be tested against historical data stored to thehistorical data store 336 in order to determine a probability forsuccess. For example, the team analytics system 346 may be configured toallow a team to virtually test an individual play intended to be runfrom a given offensive formation against defenses that were historicallyrun against such offensive formation. As will be apparent to one ofordinary skill in the art in view of this disclosure, the team analyticssystem 346 may be configured to allow a team to virtually test its gameplan against another team, specific players, specific formations,specific blocking protections, specific blitz packages, specific weatherconditions, and the like.

In one embodiment, the team analytics system 346, or any other engine orsystem, may be configured with access security controls (e.g., passwordprotection schemes, etc.) sufficient to limit access to team proprietarydata (e.g., game plan information, player injury data, etc.) toindividual teams. In this regard, game integrity may be preserved byensuring that proprietary data of a first team is not obtained by acompeting second team.

In some embodiments, the event engine 322 and its corresponding outputsystems (i.e., the visualization system 340, the game operations system342, the camera control system 344, the team analytics system 346, theleague analytics system 348, the statistics system 350, the XML feed/IMfeed system 352, and the historical data store/engine 336) may beconfigured to provide different levels of specificity for the outputdata. For example, an individual team may receive output data breakingdown the specific details for each play and the player dynamics for theplay, such that the team may determine the performance of each player inexecuting the specifics of a play versus an intended design. Incontrast, similar yet less detailed output may be provided to all teamssuch as basic play diagrams and standard statistics for the players.

In some embodiments, one or more of the engines shown in FIG. 3, suchas, without limitation, the team formation engine, the play engine, theevent engine, or the like, may output lists or ranked lists of probableoutput events (e.g., locations, formations, plays, events, etc.) fordisplay to a user via a graphical user interface (e.g., PC, tablet,mobile device, etc.) and/or for use by downstream engines or systems. Inother embodiments, the above described engines may select from theranked list of probable events a most probable event, or more simply a“probable event” (e.g., probable location, probable formation, probableplay, probable blocking technique, probable passing route, etc.), thateither has the highest probability indicator among the ranked list orhas a probability indicator above a pre-defined threshold.

In some embodiments, the user may validate or confirm an output event(e.g., a location, a formation, a play, or an event) to improve systemoperation. For example, in one embodiment, the event engine 322 maydetermine that the following events may have occurred each with arespective probability indicator shown in parenthesis: completed pass—12yard gain for the offense (68%); completed pass—10 yard gain for theoffense (21%); incomplete pass—0 yard gain for the offense (19%). Thisranked list may be displayed to an official via a mobile device who mayselect and confirm the correct output event, which in this example isthe completed pass for a 12 yard gain for the offense. In this regard,as will be apparent to one of ordinary skill in the art in view of thisdisclosure, the system may employ a user to break ties or close calls(e.g., probabilities within 10 percent, etc.) or to improve the accuracyof models, input weighting allocations, and the like.

In still other embodiments, the performance and analytics system maydetermine or predict participant locations, formations, plays, or otherevents despite temporary or sustained losses of blink data for one ormore tags (e.g., due to transmission failures associated with multipatheffects, line-of-sight blockages, etc.). For example, in one embodiment,the performance and analytics system: receives first tag location datafor a first participant (e.g., a ball carrier) during a first timeperiod (e.g., an in-play period representing the first 3 seconds of aplay); receives subsequent first tag location data for the firstparticipant during a second time period (e.g., a second in-play periodrepresenting the second 3 seconds of a play); receives second taglocation data for a second participant (e.g., the ball carried by theball carrier) during the first time period; and determines (or predicts)subsequent second tag location data for the second participant duringthe second time period based at least on: the first tag location datafor the first participant during the first time period, the subsequentfirst tag location data for the first participant during the second timeperiod, and the second tag location data for the second participantduring the first time period.

The above determination or prediction may be further improved using tagderived data and sensor derived data. For example, the performance andanalytics system may receive first sensor derived data (e.g.,time-of-flight sensor data or other tag and sensor derived datasuggestive of a relative proximity between the first participant and thesecond participant) for the first participant during the first timeperiod; receive subsequent first sensor derived data for the firstparticipant during the second time period; and determine the subsequentsecond tag location data for the second participant during the secondtime period further based at least on: the first sensor derived data forthe first participant during the first time period, and the subsequentfirst sensor derived data for the first participant during the secondtime period.

In still other embodiments, the above determination or prediction ofsecond participant location may be improved by comparing participantlocation at various times to formation and/or play models. Suchcomparisons may further include field data, and participant role data.For example, if we maintain the above example whereby the firstparticipant is a ball carrier and the second participant is a ball, theperformance and analytics system may determine or predict the locationof the ball (i.e., in circumstances where tag or sensor transmissionsfrom the ball are blocked) during a pre-snap period by determining thatthe ball carrier is aligned in a stationary location in the backfield.By comparing such ball carrier location data to formation models, thesystem may determine that the ball is most likely positioned at the lineof scrimmage proximate the center.

Similarly, in another embodiment, perhaps where the first participant isa quarterback and the second participant is a left guard, theperformance and analytics system may determine or predict the locationof the left guard in any given play or time period based upon comparingmovements of the quarterback to formation and play models. For example,quarterback movement from a snap position to a drop back passingposition may be suggestive that the left guard is positioned in a passblocking position proximate the line of scrimmage. Alternatively,quarterback movement from a snap position to a hand-off position may besuggestive that the left guard is positioned up field of the line ofscrimmage in a run blocking position.

FIG. 5 illustrates example participant (e.g., player) tracking over timein accordance with some embodiments of the present invention. Morespecifically, FIG. 5 illustrates the changing position of an offensiveteam during game action. Such tracking of changing positions may beuseful for various engines of the present system including, withoutlimitation, the player dynamics engine, the formation engine, the playengine, and the event engine. For example, at a first time, t−1 (e.g.,game clock: 12:26, play clock: 38 seconds, universal clock: 16:09:30),the tag location data may indicate that the tracked offensive playersare positioned well behind the line of scrimmage, thus, suggesting a lowprobability of any present formation. However, at a second time, t0(e.g., game clock: 12:01, play clock: 13 seconds, universal clock:16:09:55), certain of the tracked players (e.g., offensive linemen andreceivers) appear to have positioned themselves proximate the line ofscrimmage, thus, suggesting a higher probability of a pro set offensiveformation. At a third time, t1 (e.g., game clock: 11:55, play clock: 07seconds, universal clock: 16:10:01), certain tracked players (e.g., thereceivers and the quarterback) move away from the line of scrimmage,thus, suggesting that a play has begun. Additional times t2 through t5may be similarly tracked as shown and used by the various engines tohypothesize the occurrence of particular events (formations, playstart/stop, penalties, etc.). The play start and end times determined bythe play engine 318 may be used to determine the time period for whichthe location data is utilized for each play event. The tag location datarecorded at times t−1 through t5 may provide for a data streamindicating the motions/paths of the various players throughout theduration of a play period. It is noted that FIG. 5 does not illustratetracking of all the players after t0, or the defensive team players, inorder to simplify the illustration. Further, Audio data may beassociated with location data. For example, at t−1 the audio data may beplayers talking, formation callouts, or the like indicating that a playis forming. At t2, the audio data may be an official whistle,quarterback cadence, snap or the like indicating a play has begun. Att−2 through t3 the audio data may include shouting, impact noises, orthe like indicating a play is in progress. At t5, the play noise mayinclude an official whistle, a drop in impact noise, or the likeindicating a play has ended.

The team formation engine 312 and/or play engine 318 may analyze playerdynamics of multiple players, both offensive and defensive,simultaneously in hypothesizing the possible formations, plays, etc. Forexample, as discussed briefly above, the formation engine 312 and/orplay engine 318 may apply different weights to the tag/sensor/locationdata received for each player based in part on the player's role versusthe formation models or play models, as all the individual playerdynamics may not fully correlate to a particular formation or play. Theformation engine 312 and/or play engine 318 may then analyze thedifferent models and choose the model, or set of models, that have thehighest probability of being accurate based on the weights of all thecombined inputs.

Example Process or Method Embodiments

FIG. 6 illustrates a flowchart of an exemplary process for determiningan event based on audio data and/or location data in accordance withsome embodiments of the present invention. The process may start at 702,where a tag ID/filter (e.g. tag ID/filter 304 as shown in FIG. 4) maycorrelate one or more tags (e.g., tags 102 as shown in FIG. 1) to aparticipant (e.g., a player, official, ball, etc.) based on atag-individual correlator. Additionally, in some embodiments, one ormore sensors (e.g., sensors 203 as shown in FIG. 2A-C) may be correlatedto a participant at 504 based on a sensor—individual correlator. Thetags 102 and sensors 203 may be attached to participants, such as toplayers, officials, balls, field markers, penalty flags, other gameequipment, and reference markers on a field of play (e.g., boundarydefining reference markers). For example, in the case of players orofficials, the tags and/or sensors may be attached to equipment,uniforms, etc., worn or carried by the players or officials.

At 704, the tag ID/filter 304 may correlate one or more audio sensors(e.g., audio sensors 105 as shown in figure for sensors 203 as shown inFIG. 2) correlated with a participant or field location based on asensor-individual correlator. For illustration purposes, FIG. 7 depictsaudio sensor 105 separately from sensor 203; however, as discussedabove, audio sensor 105 is simply one type of sensor 203.

At 706, blink data may be received from the one or more tags 102 by thereceivers (e.g. receivers 106 as shown in FIG. 1). Additionally at 708,in some embodiments, other tag derived data and sensor derived data,such as audio data from sensors 203 associated with the participant, maybe received with the blink data. Audio data may also be receivedseparately from the tag blink data by receivers 106.

In another embodiment the audio data and tag location data may bereceived from a historical database (e.g. historical database 336) orother memory, or from the locate engine (e.g. locate engine 302 as shownin FIG. 4).

At 710, the event engine (e.g. event engine 322 as shown in FIG. 4), orthe play engine (e.g. play engine 318 as shown in FIG. 4) may compareaudio data to audio profiles. Audio profiles may be comprised of singlesounds, such as, an official whistle, impact noise, official call,cheer, scream, yell, or the like; or compilation of sounds such as 1)official whistle, 2) quarterback cadence, 3) snap, 4) impact noise, 5)official whistle. The audio data may be required to be from specifiedaudio sensor 105 (e.g., official whistle) or any available audio sensor.For audio profiles that require a compilation of sounds the sounds maybe in a required order, a partially required order, or unspecified.

At 712, the event engine 322 or play engine 318 may determine an eventprobability for each audio profile. The compared audio profiles areassigned a probability value based on the correlation of the audio datato the audio profile. In an instance in which the event may bedetermined based on audio data alone the process may continue at 728. Ifthe event requires both audio and location data for determination theprocess may continue 714.

At 714, tag location data may be determined by the locate engine 302from the blink data as discussed in FIG. 1-4.

At 716, the play engine 318 or the event engine 322 may compare taglocation data to event models. For example an event model may requireplayers assembling on a line of scrimmage, indicating a play forming,players or at least key player roles (e.g. quarterback, linemen, or thelike) to stop or pause for a period indicating a play start, rapidmovement of players toward and across the line of scrimmage indicating aplay in progress, and a secession of motion of players and/or the objectball indicating an end of a play or event.

At 718 the event engine 322 or play engine 318 may determine an eventprobability based on the correlation of the location data to the eventmodel. The event engine 322 or play engine may assign a probability tothe play model(s). In an instance in which the event may be determinedby location data probability alone the process may continue at 728.

At 720, time data may be associated with the location data. The locateengine 302 may associate a time or time stamp with the receipt of blinkdata which may be correlated to the location data. In anotherembodiment, time data (e.g. universal clock 385 and game clock 380 shownin FIG. 4) is associated with or time stamps the location data by theevent engine 322.

At 722, time data may be associated with the audio data. The locateengine 302 may associate a time or time stamp with the receipt of theblink data which may be correlated to the audio data. In anotherembodiment, time data (e.g., universal clock 385 and game clock 380shown in FIG. 4) is associated with or time stamps the audio data by theevent engine 322.

At 724, the event engine 322 may synchronize location data and audiodata by correlating the universal clock 385, game clock 380, or locateengine time stamps. The event engine 322 may perform a synchronizationcross check with time data associated by the locate engine 304 toincrease accuracy.

At 725, event engine 322 or the play engine 318 may determine an eventprobability based on audio data and location data. The event engine 322or play engine 318 may compare the audio data and location data to anevent model which correlates to an audio profile. The event engine 322or play engine 318 may assign a probability value to the event modelbased on the correlation of audio and location data to the event model.For example, the play engine 318 may determine in an instance in whichthe players move toward the line of scrimmage in conjunction with playcalls and player talking. The play engine 318 may determine in aninstance in which it is most likely that a pre-snap formation at theline of scrimmage has occurred (e.g., an offensive team has aligned in a“pro set” formation and a defensive team has aligned in a “3-4”formation) indicating a play is about to begin in conjunction with anofficial whistle and a quarterback cadence. The play engine 318 maythereafter determine that the offensive and defensive players have begunrapidly accelerating towards and across a line of scrimmage inconjunction with an official whistle and quarterback cadence therebyindicating that a play has begun. The play engine 318 may furtherdetermine that an offensive player has been tackled by a defensiveplayer in conjunction with impact noise and an official whistle therebyindicating that a play has concluded.

In another embodiment the event engine 322 or the play engine 318averages the event probabilities of the audio profiles and event models.The averaging of probability values may be weighted to give a more valueto location or audio data. For example the location data may be weightedat 7 and the audio data may be weighted as 3 resulting in location beingdominate in the probability and therefore being dominate in the eventdetermination.

At 726, the event engine 322 or the play engine 318 may analyze eventprobabilities based on audio data and/or location data. The event engine322 and play engine 318 may compare the probability values assigned toevent models and audio profiles to predetermined threshold values. Insome embodiments the predetermined threshold values may be applicable toall event models. In other embodiments, the predetermined thresholds maybe assigned based on each individual event model or event model type.For example, official calls, plays, formations, or the like may have ahigher threshold value than profiles indicative of an injury. Forexample, an injury event may be determined by a 75 percent probabilityof audio data containing a scream may satisfy the predeterminedthreshold. In another example the audio data may be a 80 percentprobability of containing an official whistle, a quarterback cadence, asnap, impact noise, and an official whistle; and the location data maycontain participants lining up at the line of scrimmage, a pause inmotion of specified players, (e.g. quarterback, linemen, or the like,rapid movement of the object ball and participant players, and asecession of motion of the object ball may be required to satisfy thepredetermined threshold.

At 728, the event engine 318 and the play engine 322 may determine anevent based on event probabilities. In an instance in which the eventengine 322 or the play engine 322 determine that an event profile hassatisfied a predetermined value an event is generated. An event maycomprise, without limitation, an official call, a play, a formation, atouchdown, a field goal, an injury, a change of possession, weatherconditions, field position change, downs, or the like. In an instance inwhich an event is not determined the process may continue at 706 or 708.

At 730, the event, location data, and/or audio data may be output to amemory. Memory may include, but not limited to, dynamic models database310, formation models database 316, play models database 320, play eventaudio profiles database 320, official models database 328, historicaldatabase 336, or the like.

At 732, the event may be output to one or more user interfaces. Userinterfaces may include but not limited to, desktop computers, laptopcomputers, personal data assistants (PDAs), tablet computers, electronicreaders, pagers, mobile phone, smart phones, or the like.

At 734, the event may be output to subsystems. Subsystems may includewithout limitation, a visualization system 340, a game operations system342, a camera control system 344, a team analytics system 346, a leagueanalytics system 348, a statistics system 350, an XML feed and/orinstant message feed 352, a historical data store/engine 336, or othersystems as may be apparent to one of ordinary skill in the art in viewof this disclosure.

Example Processing Apparatus

FIG. 7 shows a block diagram of components that may be included in anapparatus that may determine events in accordance with embodimentsdiscussed herein. Apparatus 1200 may comprise one or more processors,such as processor 1202, one or more memories, such as memory 1204,communication circuitry 1206, and user interface 1208. Processor 1202can be, for example, a microprocessor that is configured to executesoftware instructions and/or other types of code portions for carryingout defined steps, some of which are discussed herein. Processor 1202may communicate internally using data bus, for example, which may beused to convey data, including program instructions, between processor1202 and memory 1204.

Memory 1204 may include one or more non-transitory storage media suchas, for example, volatile and/or non-volatile memory that may be eitherfixed or removable. Memory 1204 may be configured to store information,data, applications, instructions or the like for enabling apparatus 1200to carry out various functions in accordance with example embodiments ofthe present invention. For example, the memory could be configured tobuffer input data for processing by processor 1202. Additionally oralternatively, the memory could be configured to store instructions forexecution by processor 1202. Memory 1204 can be considered primarymemory and be included in, for example, RAM or other forms of volatilestorage which retain its contents only during operation, and/or memory1204 may be included in non-volatile storage, such as ROM, EPROM,EEPROM, FLASH, or other types of storage that retain the memory contentsindependent of the power state of the apparatus 1200. Memory 1204 couldalso be included in a secondary storage device, such as external diskstorage, that stores large amounts of data. In some embodiments, thedisk storage may communicate with processor 1202 using an input/outputcomponent via a data bus or other routing component. The secondarymemory may include a hard disk, compact disk, DVD, memory card, or anyother type of mass storage type known to those skilled in the art.

In some embodiments, processor 1202 may be configured to communicatewith external communication networks and devices using communicationscircuitry 1206, and may use a variety of interfaces such as datacommunication oriented protocols, including X.25, ISDN, DSL, amongothers. Communications circuitry 1206 may also incorporate a modem forinterfacing and communicating with a standard telephone line, anEthernet interface, cable system, and/or any other type ofcommunications system. Additionally, processor 1202 may communicate viaa wireless interface that is operatively connected to communicationscircuitry 1206 for communicating wirelessly with other devices, usingfor example, one of the IEEE 802.11 protocols, 802.15 protocol(including Bluetooth, Zigbee, and others), a cellular protocol (AdvancedMobile Phone Service or “AMPS”), Personal Communication Services (PCS),or a standard 3G wireless telecommunications protocol, such as CDMA20001× EV-DO, GPRS, W-CDMA, LTE, and/or any other protocol.

The apparatus 1200 may include a user interface 1208 that may, in turn,be in communication with the processor 1202 to provide output to theuser and to receive input. For example, the user interface may include adisplay and, in some embodiments, may also include a keyboard, a mouse,a joystick, a touch screen, touch areas, soft keys, a microphone, aspeaker, or other input/output mechanisms. The processor may compriseuser interface circuitry configured to control at least some functionsof one or more user interface elements such as a display and, in someembodiments, a speaker, ringer, microphone and/or the like. Theprocessor and/or user interface circuitry comprising the processor maybe configured to control one or more functions of one or more userinterface elements through computer program instructions (e.g., softwareand/or firmware) stored on a memory accessible to the processor (e.g.,memory 1204, and/or the like).

In some embodiments, certain ones of the operations above may bemodified or further amplified as described below. Moreover, in someembodiments additional optional operations may also be included. Itshould be appreciated that each of the modifications, optional additionsor amplifications below may be included with the operations above eitheralone or in combination with any others among the features describedherein.

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Moreover, although the foregoing descriptions and the associateddrawings describe example embodiments in the context of certain examplecombinations of elements and/or functions, it should be appreciated thatdifferent combinations of elements and/or functions may be provided byalternative embodiments without departing from the scope of the appendedclaims. In this regard, for example, different combinations of elementsand/or functions than those explicitly described above are alsocontemplated as may be set forth in some of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

1.-24. (canceled)
 25. A method comprising: determining, using aprocessor, tag location data based on blink data and time measurements,the blink data generated by a plurality of location tags carried byparticipants, the blink data received at a plurality of receiversdisposed about a monitored area including the location tags;synchronizing, using the processor, the tag location data and audiodata, the audio data generated by a sensor in the monitored area;determining, using the processor, an event probability based on both theaudio data and the tag location data by: identifying a status of a playbased on the audio data, wherein the status of the play is one or moreof play starting, play in progress, or play ended; and identifying atype of movement of participants based on the tag location data; andgenerating, using the processor, an event based on the event probabilitysatisfying a threshold.
 26. A method as defined in claim 1, wherein thesensor is affixed to a particular participant, and further comprisingrequiring the audio data to be received from the sensor affixed to theparticular participant.
 27. A method as defined in claim 1, whereinidentifying the status of the play based on the audio data comprisescomparing the audio data to a quarterback cadence profile.
 28. A methodas defined in claim 1, wherein identifying the status of the play basedon the audio data comprises comparing the audio data to a whistleprofile.
 29. A method as defined in claim 1, wherein identifying thestatus of the play based on the audio data comprises comparing the audiodata to an impact noise profile.
 30. A method as defined in claim 1,wherein synchronizing the audio data and the tag location datacomprises: associating the tag location data with the audio data; andassociating the time data with the tag location data.
 31. A method asdefined in claim 1, wherein identifying the type of movement ofparticipants based on the tag location data comprises comparing the taglocation data to event models.
 32. A method as defined in claim 1,wherein the audio data is associated with an official and the taglocation data is associated with players.
 33. A computer program productfor determining events, the computer program product comprising anon-transitory computer readable storage medium and computer programinstructions stored thereon, the computer program instructionscomprising program instructions at least configured to: determine taglocation data based on blink data and time measurements, the blink datagenerated by a plurality of location tags carried by participants, theblink data received at a plurality of receivers disposed about amonitored area including the location tags; synchronize the tag locationdata and audio data, the audio data generated by a sensor in themonitored area; determining an event probability based on both the audiodata and the tag location data by: identifying a status of a play basedon the audio data, wherein the status of the play is one or more of playstarting, play in progress, or play ended; and identifying a type ofmovement of participants based on the tag location data; and generate anevent based on the event probability satisfying a threshold.
 34. Acomputer program product as defined in claim 33, wherein the sensor isaffixed to a particular participant, the audio data is required to bereceived from the sensor affixed to the particular participant.
 35. Acomputer program product as defined in claim 33, wherein the computerprogram instructions are configured to identify the status of the playbased on the audio data by comparing the audio data to a quarterbackcadence profile.
 36. A computer program product as defined in claim 33,wherein the computer program instructions are configured to identify thestatus of the play based on the audio data by comparing the audio datato a whistle profile.
 37. A computer program product as defined in claim33, wherein the computer program instructions are configured to identifythe status of the play based on the audio data by comparing the audiodata to an impact noise profile.
 38. A computer program product asdefined in claim 33, wherein the computer program instructions areconfigured to synchronize the audio data and the tag location data by:associating the tag location data with the audio data; and associatingthe time data with the tag location data.
 39. A computer program productas defined in claim 33, wherein the computer program instructions areconfigured to identify the type of movement of participants based on thetag location data by comparing the tag location data to event models.40. A computer program product as defined in claim 33, wherein the audiodata is associated with an official and the tag location data isassociated with players.
 41. An apparatus for determining eventscomprising at least one processor and at least one memory includingcomputer instructions configured to, in cooperation with the at leastone processor, cause the apparatus to: determine tag location data basedon blink data and time measurements, the blink data generated by aplurality of location tags carried by participants, the blink datareceived at a plurality of receivers disposed about a monitored areaincluding the location tags; synchronize the tag location data and audiodata, the audio data generated by a sensor in the monitored area;determine an event probability based on both the audio data and the taglocation data by: identifying a status of a play based on the audiodata, wherein the status of the play is one or more of play starting,play in progress, or play ended; and identifying a type of movement ofparticipants based on the tag location data; and generate an event basedon the event probability satisfying a threshold.
 42. An apparatus asdefined in claim 41, wherein the sensor is affixed to a particularparticipant, and the audio data is required to be received from thesensor affixed to the particular participant.
 43. An apparatus asdefined in claim 41, the at least one memory and the computer programinstructions configured to, in cooperation with the at least oneprocessor, cause the apparatus to identify the status of the play basedon the audio data by comparing the audio data to a quarterback cadenceprofile.
 44. An apparatus as defined in claim 41, wherein the at leastone memory and the computer program instructions configured to, incooperation with the at least one processor, cause the apparatus toidentify the status of the play based on the audio data by comparing theaudio data to a whistle profile.
 45. An apparatus as defined in claim41, the at least one memory and the computer program instructionsconfigured to, in cooperation with the at least one processor, cause theapparatus to identify the status of the play based on the audio data bycomparing the audio data to an impact noise profile.
 46. An apparatus asdefined in claim 41, the at least one memory and the computer programinstructions configured to, in cooperation with the at least oneprocessor, cause the apparatus to synchronize the audio data and the taglocation data by: associating the tag location data with the audio data;and associating the time data with the tag location data.
 47. Anapparatus as defined in claim 41, the at least one memory and thecomputer program instructions configured to, in cooperation with the atleast one processor, cause the apparatus to identify the type ofmovement of participants based on the tag location data comprisescomparing the tag location data to event models.
 48. An apparatus asdefined in claim 41, wherein the audio data is associated with anofficial and the tag location data is associated with players.